Method for automatically detecting sepsis and displaying dynamic sepsis images in a hospital room

ABSTRACT

A system for automatically detecting and displaying sepsis images in a hospital system is described herein. In some examples, the system can include receiving data related to a plurality of physiological parameters from a patient over time, and identifying and organizing patterns of perturbations of the data. The system can also include analyzing the perturbations to identify known relational physiologic patterns and detecting a pattern of sepsis, the pattern being comprised of a plurality of perturbations and/or relational physiologic patterns. Furthermore, the system can include organizing and synthesizing the plurality of relational physiologic patterns into a set of sepsis images, which as an aggregate whole make up an image representation of the complex and dynamic state of sepsis, and displaying the sepsis images. The images can be color motion images of cascades which display the severities of individual perturbations and relational physiologic patterns along the cascade pattern.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/844,381, filed Mar. 15, 2013, which is a continuation of U.S. patentapplication Ser. No. 12/437,417 filed May 7, 2009, which claims thebenefit of U.S. Provisional Application No. 61/126,906, filed May 8,2008 and this application also claims the benefit of U.S. ProvisionalApplication No. 61/200,162, filed Nov. 25, 2008, the disclosures ofwhich are hereby incorporated by reference in their entirety for allpurposes. This application is a continuation-in-part of U.S. patentapplication Ser. No. 12/437,385 filed May 7, 2009 entitled “MedicalFailure Pattern Search Engine” the disclosure of which is herebyincorporated by reference in its entirety for all purposes. Thisapplication is also a continuation in part of U.S. patent applicationSer. No. 12/629,407 entitled “Microprocessor System for the Analysis ofPhysiologic and Financial Datasets,” filed Dec. 2, 2009 which is acontinuation of U.S. patent application Ser. No. 10/150,842, entitled“Microprocessor System for the Analysis of Physiologic and FinancialDatasets,” filed May 17, 2002, now U.S. Pat. No. 7,758,503, thedisclosure of which is hereby incorporated by reference in its entiretyfor all purposes, which claims the benefit of U.S. ProvisionalApplication Ser. No. 60/291,687 filed May 17, 2001, the contents ofwhich are hereby incorporated herein by reference and U.S. ProvisionalApplication Ser. No. 60/291,691, filed on May 17, 2001. This applicationis also a continuation in part of U.S. patent application Ser. No.11/369,355, entitled “Centralized hospital monitoring system forautomatically detecting upper airway instability and for preventing andaborting adverse drug reactions”, filed Mar. 7, 2006, which is acontinuation of U.S. patent application Ser. No. 10/150,582 entitled“Centralized hospital monitoring system for automatically detectingupper airway instability and for preventing and aborting adverse drugreactions,” filed May 17, 2002 now U.S. Pat. No. 7,081,095, which claimsthe benefit of U.S. Provisional Patent Application Ser. Nos. 60/291,691and 60/291,687, both filed May 17, 2001 and U.S. Provisional PatentApplication Ser. No. 60/295,484 filed Jun. 1, 2001, the disclosures andcontents of which are incorporated by reference as if completelydisclosed herein. This application is also a continuation-in-part andclaims priority to U.S. Patent Application Ser. No. 12/152,747 entitled“Pulse Oximetry Relational Alarm System for Early Recognition ofInstability and Catastrophic Occurrences,” filed May 16, 2008, thedisclosure of which is hereby incorporated by reference in its entiretyand for all purposes.

BACKGROUND

The present disclosure relates systems and methods for detecting andmonitoring patient conditions in clinical medicine settings.

Patient care in a hospital setting involves a complex management processbecause healthcare workers address multiple patient issuessimultaneously. Decisions about patient priority and care made by thehealthcare workers are subjective to some degree and may vary dependingon the level of expertise and experience of each person involved inpatient care. In addition, patients' complaints and symptoms are oftencomplex, because a disease process may have its own associatedcomplications, and a disease may also affect other concurrent patientconditions. Patients may also bring with them a degree of subjectivityin describing their symptoms, which may generate both variableindications of clinical conditions.

Uncontrolled complexity is a cause of large numbers unnecessary death inhospitals. Unfortunately hundreds of common but subtle modes of failurewhich lead to complications and death can potentially occur with everypatient in the hospital. However, present hospital patient monitoringdevices are entirely insufficient relative this level of complexity.There is an acute need for a quantum advance in patient data processingwhich is capable of managing the actual pathophysiologic complexitypresent. Without such technology vast numbers of unnecessary deaths canbe expected to continue in hospitals, unabated and worldwide.

One common example of the challenges involved in detecting complexpatient conditions, is undetected septic shock. Whether or not a givenpatient with an infection progresses to shock often depends on a complexrelationship of patient-specific physiologic responses to immunologicand inflammatory perturbation as well as the physiologic state of thepatient at the onset and during the perturbation and the timeliness andadequacy of intervention (e.g. antibiotics and/or fluid). These factorsinteract to define the dynamic state of the patient. This level ofcomplexity, evolving as a “silent” mechanism of death on a busy hospitalward, represents a major threat to patients worldwide.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the present disclosure may become apparent upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 is an example of a component diagram of a patient demonstratingthe overlapping patient complexities that may be used to constructrelational binaries and images for detection;

FIG. 2 is a diagram depicting the levels of analysis in accordance withan example of a embodiment;

FIG. 3A is a data flow diagram in accordance with an example of aembodiment;

FIG. 3B is a diagram of an example of a system in accordance with anexample of a embodiment;

FIG. 3C is a data and action flow diagram in accordance with an exampleof a embodiment;

FIG. 4 is an example of a UML static diagram of a time series matrixwithin one embodiment of a relational binary processor;

FIG. 5 is an example of a UML static diagram of a subset of classeswithin a patient safety processor specifically defining a subset of theresultant occurrences and related elements from an analysis;

FIG. 6 is an example of a UML static diagram of a subset of classeswithin a patient safety processor specifically modeling the binariesresultant from an analysis;

FIG. 7A is a UML static diagram of a subset of classes within thepatient safety processor specifically modeling the an occurrencedefinition set used by the patient safety processor to identify,construct and qualify occurrences;

FIG. 7B is a UML static diagram of a subset of classes within thepatient safety processor specifically modeling the an occurrence typesused to link created occurrences with their definition;

FIG. 8A is a UML static diagram of a subset of classes within thepatient safety processor specifically modeling the an occurrencedefinition set used by the patient safety processor to identify,construct and qualify events;

FIG. 8B is a UML static diagram of a subset of classes within thepatient safety processor specifically modeling the an occurrencedefinition set used by the patient safety processor to identify,construct and qualify relational binaries;

FIG. 8C is a UML static diagram of a subset of classes within thepatient safety processor specifically modeling the an occurrencedefinition set used by the patient safety processor to identify,construct and qualify images;

FIG. 9 is a user interface model of the convergence editor that may beused to visually construct and persist the binary definition setdepicting a binary diagram within the convergence editor that pertainsto the monitoring of sleep apnea;

FIG. 10 is a user interface model of the image editor that may be usedto visually construct and persist the image definition set depicting aimage diagram within the image editor that defines narcotic-inducedventilation instability;

FIG. 11 is an additional view of the user interface model of theconvergence editor specifically depicting a binary diagram within theconvergence editor that pertains to the monitoring heparin therapy;

FIG. 12 is an additional view of the user interface model of theconvergence editor specifically depicting a binary diagram within theconvergence editor that pertains to the monitoring insulin therapy;

FIG. 13 is a third view of the user interface model of the convergenceeditor specifically depicting a binary diagram within the convergenceeditor that pertains to the monitoring narcotic therapy;

FIG. 14 is a user interface model of the image editor that may be usedto visually construct and persist the image definition set showing animage diagram within the image editor that defines heparin-inducedhemorrhage;

FIG. 15A is an image frame with a plurality of timelines organized intogroupings, which shows an image of an expanding cascade of septic shock;

FIG. 15B is an image frame with a plurality of timelines organized intogroupings, which shows a image of an expanding cascade of septic shockwith portions of the image being separated into sequential states;

FIG. 15C is a image frame with a plurality of timelines organized intogroupings, showing an image of an expanding cascade of severe septicshock an early image of septic shock as presented in real time todemonstrate that there is little in these first perturbations to warn ofthe impending deadly cascade;

FIG. 15D is an image frame that shows an image of a failure cascadesevere septic shock as presented in real time to demonstrate the earlyimage of inflammatory, hemodynamic, and respiratory augmentation, withearly immune failure;

FIG. 15E is an image frame that shows an image of a failure cascade ofsevere septic shock as presented in real time to demonstrate the imageof inflammatory, hemodynamic, and respiratory augmentation, with immunefailure, but now with evidence of decline in respiratory gas exchangeand fall in platelet count;

FIG. 15F is an image frame that shows an image of an advanced cascade ofsevere septic shock as presented in real time to demonstrate progressionto metabolic failure, renal failure, hemodynamic failure, andrespiratory failure;

FIG. 16 is a general image including a plurality of timelines organizedinto groupings, which shows a image of congestive heart failure;

FIG. 17 is a general image including a plurality of timelines organizedinto groupings, which shows a image of sleep apnea;

FIG. 18 is a general image including a plurality of timelines organizedinto groupings, which shows a image of thrombotic thrombocytopenicpurpura;

FIG. 19 shows overview image of perturbation onset and progression asderived from the time lapsed MPPC of FIG. 15A wherein the perturbationsin each grouping are incorporated into an aggregate index along a singlesmoothed time series for each group;

FIG. 20 is a split screen diagram of a drag and drop interface forconstructing combined physiologic and treatment images for subsequentdetection by the patient safety processor showing the construction of amotion picture indicative of narcotic associated recovery failure in thepresence of sleep apnea;

FIG. 21 is an image frame of a image editor for constructing a MPPC forrecognition by the Patient safety processor consistent with presumptivesevere sepsis;

FIG. 22 is a general image including a plurality of timelines from thepatient illustrated in FIG. 1, which shows a image of excessivesecretion of serum inappropriate antidiuretic hormone (SIADH) inducedfall in hyponatremia and confusion;

FIG. 23 is a diagram of the patient safety processor network forarchiving and cataloging images;

FIG. 24 is a data flow diagram of the patient safety processor networkshowing the input of image data into the centralized database, the useof guided image discovery to improve failure recognition andprotocolization, and the distribution of safety image definitions toimplement timely failure detection and intervention;

FIG. 25 is a data flow diagram illustrating guided image discovery imagediscovery through the use of the patient safety comparison processor andthe image construction processor to produce a statistically enhancedoccurrence definition set;

FIG. 26 is a data flow diagram illustrating guided image discoverythrough the use of an image patient safety comparison processor and animage construction processor with a narrowed set of reference patientsto produce a statistically enhanced occurrence definition set;

FIG. 27 is a diagram depicting the generation of property channelswithin a repeating occurrence micro-domain;

FIG. 28 is a user interface model of the occurrence definition editordepicting an image diagram within the occurrence editor that pertains tonarcotic-induced ventilation instability;

FIG. 29 is a user interface model of the occurrence definition editordepicting an image diagram within the occurrence editor that pertains toheparin-induced hemorrhage;

FIG. 30 is a user interface model of the occurrence property editorwithin the occurrence definition editor depicting the construction of aninstability index within an oximetry cluster occurrence;

FIG. 31 is a user interface model of the occurrence property editor usedwith a selected reference patient and occurrence depicting theconstruction of an instability index within an oximetry clusteroccurrence;

FIG. 32 is a sample dependency diagram and image dependency diagramdepicting the dependencies of a heparin-induced hemorrhage image;

FIG. 33 is a user interface model of the occurrence editor specificallyconfigured to define a pattern occurrence; and

FIG. 34 is diagram of a source of time series related to a patient'sintravascular volume showing the generated time series incorporated intothe patient safety processor to generate an image suggestive ofintravascular volume depletion.

DETAILED DESCRIPTION

One or more specific embodiments of the present disclosure will bedescribed below. In an effort to provide a concise description of theseembodiments, not all features of an actual implementation are describedin the specification. It should be appreciated that in the developmentof any such actual implementation, as in any engineering or designproject, numerous implementation-specific decisions must be made toachieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

The present disclosure provides systems and methods for diagnosis,monitoring, and treatment of certain clinical conditions. Aprocessor-based system may characterize and quantify patientphysiological conditions by analyzing data relating the patient. In oneembodiment, a database of is converted into a format which is favorablefor searching and/or analysis of complex relational patterns of trendsand variations. One such format is a time series matrix which itself maybe formatted to generate an image or moving image of the abnormalcomponents of the time-series matrix that may be further processed intooperator-interpretable data.

In one embodiment the time-series matrix constructed by the processormay include a plurality of (e.g., hundreds or thousands) of individualtime-series each including different chemical, electrical, mechanical,and/or state related parametric and/or non-parametric values of anindividual patient. In one embodiment, data derived from the patient isorganized into a comprehensive set of time series each aligned along asingle time axis. Each individual time series is two-dimensional withone dimension being time but there are no other limits on seconddimensions so that the matrix may have thousands of other dimensions inaddition its unifying time dimension. For example the dimensions mayinclude derivatives and frequency measures or calculations of values,calculated or measured relationship between a pluralities of values,time relationships between a plurality of values to name a few. In a fewmore examples, the dimensions may be further defined by a instantaneousmagnitude value, a moving window derived averaged magnitude value, aninstantaneous slope of any of the measured or calculated values, andmoving window average of the slope of any of the measured values, anacceleration value or area related value, a peak or nadir value, adifference value, a recovery value, a threshold breach, a thresholdapproximation, a statistical parameter or value, a derivative value, atrend value, frequency domain derived value, to name a few.

One embodiment provides visibility into the complete matrix of patientdata by direct search of a hospital's global and/or centralized medicalrecords which may be automatic on a scheduled regular basis, continuous,triggered, or manual basis. Direct search allows rapid access toinformation relevant to the time and relationships involved complexfailure cascade images or other patterns searched for, includingoutputting an identification of the patient exhibiting the image. Theoutput can comprise a display located at the nurses station or anotherlocation and can include direct notification of the patient (as througha pager or phone attached to the patient, nurse or physician (which maybe is capable of presenting the image for review). The detectiontherefore can occur at the central database repository at a centrallocation for the hospital, hospital system, a region, or the entireworld, with the provision of direct notification to the hospital, thecaregiver, or the patient him or herself.

In one embodiment, for manual searching and/or for preparing theautomatic search process a health worker or researcher is presented witha single search box in which the name of a condition, pattern or otheroccurrence can be entered. The health worker or researcher can thendirect the patient safety processor to execute the search. Searchresults are presented in a paged display allowing for rapid scan. Witheach “hit” is presented a link to navigate into a screen of datatime-relevant to the “hit” and data relevant to the occurrences thatconstitute the bit. This may include time series displays, occurrencestream displays, patient data, data with regard to the definition of theoccurrence and/or constituent occurrences to name a few. A few of thecharacteristics of the search which can be used to narrow the view ofthe condition, pattern or other occurrence are listed below. Theprocessor may be programmed to navigate the search using thesecharacteristics. These will be discussed in more detail in thesubsequent disclosure.

Time relevance—the understanding of the relationships within thespecified occurrence provides the information to display a series oftime spans based on the overall evolution of the occurrence within theorganism. One embodiment of the patient safety processor search providesrapid switch between these time horizons to provide both a macro andmicro-view of the evolving pattern

Data relevance—using the definition of the specified occurrence oneembodiment of the patient safety processor search system may display thechannels of data relevant to the occurrence. These channels of datainclude channels in which related occurrence appear and also channels inwhich occurrences do not appear that would be expected. The relevantdata set is closely related to the time relevance and therefore iscoordinated with the time relevant horizons.

Statistical relevance—One embodiment, using the statistical information,gathered, for example, from local sources and optionally fromcentralized image data, the statistical relevance of the occurrence maybe provided. If the data is real-time data then probable paths ofevolution may be presented as well as alternative channels of data thatcan be executed to further identify the occurrence in question.

Meta-relevance—combining the statistical information with themeta-system of all possible patterns/images the patient safety processormay indicate alternative interpretations of the data, candidateoccurrences as well as “what-if” scenarios in which the health worker orresearcher indicates certain data points (lab results, etc.) as suspector provides “best guesses” on missing data.

The health worker or researcher may include filters in the search—suchas time horizons, patient populations, geographic locations oroccurrence categories to name a few. Occurrence topologies may becreated to assist in the filtering. These filters may be pre-defined toprovide typical focus areas or may be defined by the health care worker.Advanced Boolean combinations may be provided as well as simplermechanisms.

Search results may be aggregated by specific levels of granularity (e.g.patient, patient stay, geographical location to name a few). If data isaggregated then the health-care, once a “hit” has been selected, canchoose a single occurrence from the aggregation or can choose to see amulti-occurrence display (e.g. within a patient stay).

Further, an aggregation may be selected and then searched within. Inthis way the health-care worker can iteratively narrow her search untila specific occurrence or occurrence group is selected for display.

The health worker may navigate out of an occurrence or occurrence groupdisplay back into the search results. Search or simple time/dataselection may provide entry, into the matrix. Once the matrix has beenentered the processor display may center (if desired) on a particularoccurrence. The relationships maintained within the processor for thisoccurrence may provide a wide range of navigation. The health careworker may choose to navigate down into the constituent elements of theoccurrence. At each level, the display may be configured to the desiredrelevance. The health care worker is thereby provided with onealternative mechanism of essentially “zooming in” on a particular aspectof the image. The health care worker may also choose to “zoom out” bynavigating up to an occurrence (or occurrences) to which the currentoccurrence is a constituent part. In an example detected fall event inbicarbonate may comprise the occurrence from which manual or automaticnavigation zooms out to view an entire sepsis cascade of which it is apart. The higher view may show a high-level image evolution. Lateralmovement may occur not only by simple panning (e.g. slidingforward/backward in time) but also by navigating to the next similaroccurrence or the next stage of image evolution within the currentoccurrence. Navigation may also be through the meta-data. This allowsthe health worker to navigate to similar images to the ones thatactually have been identified. These images can be super-imposed forcomparison. For example, the health care worker may request to seeimages that are statistically known to be in the evolution path of thecurrent image. By navigating to expected (but not yet realized)evolution paths the health care worker can anticipate the development ofperturbation and see expected reactions to treatment.

Further, within an occurrence or occurrence group display the healthcare worker or researcher will be presented with opportunities to searchbased on elements within the display. For example, if an image containsa specific event, the health-care worker may search for otheroccurrences of that event within a timeframe or other search horizon(e.g. related to a specific physician).

The spin-off search may be modified in one or more ways from the elementfrom which the search was constructed. For example, the health careworker may select a threshold violation and indicate search, but allow aslightly higher or lower threshold. Or, as another example, a binary maybe selected but the requirement of the time between occurrences may berelaxed.

In effect, search permeates the patient safety processor system allowingfor any element or meta-element to become the launch-point for a patientsafety processor search. In this context the health worker or researchercan rapidly follow his or her intuition regarding the nature andevolution of a condition of interest allowing the patient safetyprocessor to provide substantially immediate aggregations of data inviews relevant to the relationships within the focus data, the overallstatistical relationships in the overall available body of research andthe intuition of the current health worker. The processor can beprogrammed to automatically search using one or more (or another type)of narrowing characteristics. For example the processor could beprogrammed to, upon the detection of a trend rise in ventilation of agiven patient, search for other trends, breaches, events, or images intime relevance to the detected rise on and to determine if an image(such as a cascade) or an event (such as a drug infusion or a fall inbicarbonate) is present and where in combination with the rise inventilation a new image is detected.

The individual time series of the matrix may extend the entire length ofthe matrix for example as the weight of the patient or ejection fractionof the left ventricle of a patient or may be transient, as for examplewith a single injection of an IV narcotic or may be intermittent, aswith a time series of the peak (recovery) SPO₂ values within sleep apneaclusters. In one embodiment the individual time series are linked toeach other by processing to convert the raw time series into time seriesof events (such as objects by the process of objectification) butanother method may be used. Linkage by event processing and/orobjectification produces a comprehensive (potentially omni-dimensional)objectified matrix of times series of data relating to a patient,extending along a central common time axis with extensivecross-linkages, series that are not time series (for example a series offrequencies of a parameter detected at a single point in time) may alsobe objectified, but in this case this type of series extendsperpendicular to the time axis of the matrix at the time (or window oftime) of the occurrence of that series, series which are not time serieshave the same time designation for the entire series and, like timeseries, they may be linked with any other time series or other series inthe matrix.

In one embodiment events, relational events, and aggregated events aredefined to construct images of physiologic failure and care.Identifications of modes of physiologic failure and care by the analysisof the images provides for earlier recognition and intervention andimproved protocolization of testing and treatment. A processing networkprovides for the development of extensive archives of images ofphysiologic failure and care to provide for processed integration of theinternational experience into the image recognition protocols to enhanceand accelerate real-time physiologic image recognition and to improvethe cost effectiveness of testing and treatment including thosehospitals in remote and/or less well served communities.

One embodiment includes a real-time processing method for searching forand detecting physiologic occurrences, physiologic failure and/or carehaving steps of: (1) converting medical records into at least one timeseries matrix of a particular configuration suitable for imaging (forexample the configuration may be a 2 or more dimensional spatialconfiguration, and/or a 2 or more dimensional temporal configuration,and/or 2 or more dimensional frequency configuration, and/or anotherconfiguration suitable for imaging); (2) imaging the matrix to detect atleast one image indicative of physiologic occurrences, physiologicfailure, and/or care (which can for example include a sepsis cascadepattern, a sepsis shock pattern, a drug reaction pattern, to name afew); and (3) taking action based on the detection of the image whichcan for example, include outputting an indication (which can be analarm) of the image and/or the likely cause of the image.

In one embodiment the time series matrix is processed (and thisprocessing may be provided as part of the construction of the timeseries matrix to generate and time series matrix of events. In anembodiment these events are objects (such as objects having a relationalhierarchy by the process of time-series objectification) therebyrendering an objectified time series matrix. The raw time series matrixand/or the time series matrix of events and/or the objectified timeseries matrix may be rendered in a particular configuration such as aparticular default digital spatial and/or temporal and/or frequencyand/or statistical configuration (to name a few), and then digitallyimaged and the image characterized by known digital image recognitionmethods to detect a pattern and/or a plurality of patterns. These imagepatterns may be defined along the configuration of the matrix byselections of the user, by methods of digital image recognition, bystatistical processing, as by neural net processing or by other methodsas for example the methods described and/or listed in U.S. patentapplication Ser. No. 11/351,449 of the present inventors, the entiredisclosure of which is hereby incorporated by reference for all purposesas if completely disclosed herein.

One embodiment includes a real-time processing method for detectingphysiologic occurrences, physiologic failure and/or care having stepsof: (1) converting medical records into at least one time series matrixof a particular configuration suitable for imaging; processing thematrix to render an matrix of time series of events (such as anobjectified time series matrix (for example the configuration of theobjectified time series matrix may be a 2 or more dimensional spatialconfiguration, and/or a 2 or more dimensional temporal configuration,and/or 2 or more dimensional frequency configuration, and/or anotherconfiguration suitable for imaging); (2) imaging the time series matrixof events to detect at least one image indicative of physiologicoccurrences, physiologic failure, and/or care (which can for exampleinclude a sepsis cascade pattern, a sepsis shock pattern, a drugreaction pattern, to name a few); and (3) taking action based on thedetection of the image which can for example, include outputting anindication (which can be an alarm) of the image and/or the likely causeof the image. Steps one, two and three, can be combined so that the timeseries matrix is built and objectified simultaneously or the time seriesare objectified and/or imaged and then the matrix is built and thematrix is then objectified and/or imaged. A default configuration of theraw time series matrix may be displayed directly with the imagesdetected in raw form along the default configuration (as may beover-read by physicians or nurses). Alternatively, or in combination,another default configuration may be displayed for the user with thematrix presented in a processed (such as objectified) form with thedetected images being highlighted or represented or replaced as icons,motion pictures, or other visual images. In another embodiment thedetected images are reprocessed to simplify them and these reprocessedimages may be digitally imaged to detect larger and more complex images.In this way the images themselves (much like the objects in the matrix)may have an inheritance hierarchy which reduces the complexity of thedigital image recognition of the larger, complex and prolonged images.

One embodiment includes a real-time processing method having steps of:(1) converting medical records into a predetermined format forsearching, as for example at least one time series matrix; (2) definingevents such as objects (which can be relational events) along the timeseries matrix; (3) defining patterns including combinations of events,(4) using a processing search engine, searching for the events and/orthe patterns and detecting at least one complex pattern or image (whichcan for example include a sepsis cascade pattern, a sepsis shockpattern, a drug reaction pattern, to name a few); and (4) taking actionbased on the detection which can for example, include outputting anindication (which can be an alarm) of the pattern(s) and/or the likelycause of the patterns.

While an embodiment event identification and processing usingobjectification of time series matrices presently contemplated there aremany alternative processing methods which can be incorporated to defineevents or occurrences, such as pertubations, trends, variations, orthreshold violations, to name a few along the time series and along thetime series matrix to be identified by a waveform search engine (such asthe imaging processor). For example the time series matrix or the“event, trend, or pertubation processed or formatted” (such asobjectified) time series matrix may be rendered in a particularconfiguration such as a spatial configuration, and then imaged and theimage characterized by image recognition methods to detect a pattern.These image patterns may be defined along the configuration of thematrix by statistical processing, as by neural net processing or othermethods. Also the events, objects, or and/or images may be processedusing a wide range of search engine types and may be incorporated intolarger computer search engines.

Events may be defined by a wide range of methods, for example, byobjectification, by probabilistic discovery, by neural net processing,by peak and trough detection, adaptively, by objectification, by aspecific rules set, or by a single or combination of signal processingand/or characterization methods in the time domain and/or the frequencydomain and/or by other methods.

According to one embodiment, provided herein is a processing method andsystem for characterizing and quantifying complex layered non-linearsystems such as physiologic systems, using a matching layered processingarchitecture, termed an “objectified time series matrix.” Although theobjectified time series matrix is applicable to a wide range of signalprocessing environments, in an example, the matrix is applied toorganize, control, characterize and quantify substantially all aspectsthe electronic medical records (EMR) of a patient and is employed as areal-time patient safety processor. Here, the matrix is includinghundreds or thousands of parallel and, in some cases, perpendicularseries (and of segments of parallel time and perpendicular series)derived from the EMR which are programmatically bonded together in amanner to produce an embodiment which may provide real time programmaticimages which organize the complexity of the evolving processesindicative of the global and regional state of health and disease.

In one embodiment such a matrix is including hundreds or thousands ofindividual time-series each including different chemical, electrical,mechanical, and/or state related parametric and/or non-parametric valuesof an individual patient. In one embodiment all data derived from thepatient is organized into a comprehensive set of time series eachaligned along a single time axis. Each individual time series istwo-dimensional with one dimension being time but there are no otherlimits on second dimensions so that the matrix may have thousands ofother dimensions in addition its unifying time dimension. For examplethe dimensions may include derivatives and frequency measures orcalculations of values, calculated or measured relationship between apluralities of values, time relationships between a plurality of valuesto name a few. In a few more examples, the dimensions may be furtherdefined by a instantaneous magnitude value, a moving window derivedaveraged magnitude value, an instantaneous slope of any of the measuredor calculated values, and moving window average of the slope of any ofthe measured values, an acceleration value or area related value, a peakor nadir value, a difference value, a recovery value, to name a few. Theindividual time series of the matrix may extend the entire length of thematrix for example as the weight of the patient or ejection fraction ofthe left ventricle of a patient or may be transient, as for example witha single injection of a IV narcotic or may be intermittent, as with atime series of the peak (recovery) SPO₂ values within sleep apneaclusters. In one embodiment the individual time series are linked toeach other by objectification but other methods may also be used.Linkage by objectification produces a comprehensive (potentiallyomni-dimensional) matrix of times series of data relating to a patient,extending along a central common time axis with extensivecross-linkages, series that are not time series (for example a series offrequencies of a parameter detected at a single point in time) may alsobe objectified but in this case this type of series extendsperpendicular to the time axis of the matrix at the time (or window oftime) of the occurrence of that series, series which are not time serieshave the same time designation for the entire series and, like timeseries, they may be linked with any other time series or other series inthe matrix.

Also provided herein is an objectified time series matrix of patientrelated signals. A comprehensive matrix derived from a complex patientwould appear relatively opaque when viewed with hundreds or eventhousands of parallel, objectified time series with various objectifiedlinkages. Provided herein is a processing system and method for imagingthe objectified matrix of patient signals to render motion pictures ofphysiologic failure. According to one embodiment, the construction of amatrix of time series for the organization and analysis of non-linearsystems is accomplished by: (1) generating a large set of time-series ofdata relating to the nonlinear system; (2) converting the datasets intoparallel time series; (3) identifying occurrences along and between thetime series; (4) aggregating the occurrences into a real-time hierarchaldata matrix; and (5) analyzing specific nonlinear processes using thedata matrix.

According to one embodiment, the construction of an objectified matrixof time series for the organization and analysis of non-linear systemsis accomplished by: (1) generating a large set of time-series of datarelating to the nonlinear system; (2) converting the datasets intoparallel time series; (3) objectifying the time series; (4) identifyingoccurrences along and between the objects along the objectified timeseries; (5) aggregating the occurrences into an image including areal-time hierarchal data matrix of linked objects; and (6) comparingthe image to other images and/or to other values or outcomes, or imagesto determine the significance of the image.

One set of steps applied in building and using a time series matrix ofpatient related signals to detect physiologic failure includes: (1)conversion of the data set of the electronic medical records into timeseries sets, (2) linking at least a portion of the series sets; (3)defining controllable micro-domains along and between the series sets;(4) analyzing these micro-domains for occurrences, which may for examplebe a primary or relational perturbation having a specific primary orrelational slope, amplitude, polarity, state, acceleration, frequency,pattern, value, or other characteristic to name a few; (5) aggregatingthe occurrences rendering at least a programmatic image of theaggregated occurrences; (6) comparing the image to stored imagesindicative of disease; (7) taking action based on the detected image;and (8) outputting an indication based on the detected image.

In one embodiment all data relating to a given patient are convertedinto time series, parallel in time but not necessarily allcomprehensively covering the complete time span evaluated. Theobjectified time series matrix is constructed by adding each newoccurrence in a manner, which defines an inheritance-based hierarchy ofascending complexity. The objectified matrix is progressively built totransform the electronic medical records into a highly organized datastructure from which may be derived real-time motion pictures ofphysiologic condition, which allows early detection and intervention.

According to one embodiment, a patient safety processor including anobjectified time series matrix is provided by: (1) generating a largeset of time-series of data of a patient including at least data relatingto the physiologic state and/or care of a patient; (2) converting thedatasets, including at least the monitored datasets and laboratorydatasets into parallel time series; (3) objectifying the parallel timeseries; (4) converting the objectified time-series into micro-domains;(5) identifying occurrences along and between the micro-domains; (6)aggregating the occurrences into a real-time hierarchal patient safetydata matrix for the generation of motion pictures of the organizedoccurrences; and (7) recognizing and interpreting along the motionpictures, specific pathophysiologic processes or other adverse processesdefined by the motion picture.

According to one embodiment, a patient safety processor including anobjectified time series matrix is provided by; (1) generating a largeset of time-series of data of a patient including at least data relatingto the physiologic state and/or care of a patient; (2) converting thedatasets, including at least the monitored datasets and laboratorydatasets into parallel time series; (3) objectifying the parallel timeseries; (4) placing the objectified time series into an objectified timeseries matrix; (5) convening the objectified time-series intomicro-domains; identifying occurrences along and between themicro-domains; placing each stream of identified occurrences back intothe objectified time series matrix so as to progressively anditeratively increase the objectified time series; analyzing thecompleted objective time series matrix for the identification of motionpictures of the organized occurrences; and recognizing and interpretingalong the motion pictures, specific pathophysiologic processes or otheradverse processes defined by the motion picture.

According to one embodiment, a Patient safety processor (patient safetyprocessor) including a time series matrix is provided by: (1) generatinga large set of time-series of data of a patient including at least datarelating to the physiologic state and/or care of a patient; (2)converting the datasets, including at least the monitored datasets andlaboratory datasets into parallel time series; (3) identifyingrelational patterns along a plurality of time series which is indicativeof failure cascade such as, for example, a sepsis cascade, a pulmonaryembolism cascade, a metabolic cascade, and a microcirculatory cascade toname a few; (4) identifying occurrences such as, for example,inflammatory occurrences, metabolic occurrences, volumetric occurrences,hemodynamic occurrences, therapy occurrences, hematologic occurrences,respiratory occurrences (to name a few), and the timing of theoccurrences which relationally or collectively are indicative of atleast one failure cascade such as, for example, a sepsis cascade, apulmonary embolism cascade, a metabolic cascade; and (5) identifying andoutputting an indication of the cascade, the timing and type of theoccurrences along the cascade, and length of the cascade.

According to one embodiment, a patient safety processor including anobjectified time series matrix is provided by: (1) generating a largeset of time-series of data of a patient including at least data relatingto the physiologic state and/or care of a patient; (2) converting thedatasets, including at least the monitored datasets and laboratorydatasets into parallel time series; (3) objectifying the parallel timeseries; (4) placing the objectified time series into an objectified timeseries matrix; identifying occurrences such as, for example,inflammatory occurrences, metabolic occurrences, volumetric occurrences,hemodynamic occurrences, therapy occurrences, hematologic occurrences,respiratory occurrences (to name a few), and the timing of theoccurrences which relationally or collectively are indicative of atleast one failure cascade such as, for example, a sepsis cascade, apulmonary embolism cascade, a metabolic cascade; (5) identify and outputan indication of the cascade, the timing and type of the occurrencesalong the cascade, and length of the cascade.

According to one embodiment, a patient safety processor may generate atime series matrix for the early detection of septic shock or thepre-septic shock state by: (1) generating a large set of time-series ofdata of a patient including at least data relating to the physiologicstate and/or care of a patient; (2) converting the datasets, includingat least the monitored datasets and laboratory datasets into paralleltime series; (3) identifying occurrences such as, for example,inflammatory occurrences, metabolic occurrences, volumetric occurrences,hemodynamic occurrences, therapy occurrences, hematologic occurrences,respiratory occurrences (to name a few), and the timing of theoccurrences which relationally or collectively are indicative of theseptic shock or pre-septic shock failure cascade; (4) identify andoutput an indication of the septic shock or pre-septic shock failurecascade; (5) identify and output the relational timing of theinflammatory occurrence, the hemodynamic occurrence, and the respiratoryoccurrence along the cascade; (6) identify and output length of thecascade; and (7) quantify the cascade as the cascade evolves and outputa time series of the severity of the cascade.

According to one embodiment, the process of moving from a massive set oftime-series data to the recognition of, and interpretation of, a motionpicture indicative of a patient's comprehensive state of physiologicfailure and treatment includes the isolation, from the global domain ofdata, individual sets of micro-domains in which the complexity ismanageable enough for human researchers and/or software agents directedby human researchers to define and characterize specific properties andrelationships (occurrences) along and between the micro-domains whichare perceived to be, may be determined to be (and/or are statisticallyverified to be) associated with clinical conditions. The Patient Safetymatrix is then built by combining the occurrences. This process ofmatrix building from different micro domains is repeated over and overto build an extensive matrix which is including occurrences ordered intorelational timed images having an inheritance based hierarchy. Themotion picture is a timed aggregation of those images from the matrix.The motion pictures range from isolated adverse occurrences to simplephysiologic failures and finally to catastrophic pathophysiologiccascades. The global “motion picture” output of the patient safetyprocessor is a comprehensive flow of the detected images in combinationas they evolve.

One embodiment of the present techniques provides a system and methodfor the characterization, investigation, and analysis of the complexenvironment of non linear relational signals (such as physiologicsignals) through the repeated and layered use of micro-domains, which,provides a mechanism such that the micro-domains may be modeled by ahuman researcher with an expert understanding of the physiological andsystemic relationships to render detected images which are added to theobjectified matrix. One embodiment provides a mechanism by which boththe scope and the variables available within the micro-domains thatexist within a set of physiological signals may be identified, modeled,analyzed, characterized, compared and statistically investigated. Inthis embodiment, the process follows four basic steps: (1) Establish thetype and scope of a candidate micro-domain; (2) Calculate andcharacterize all or various relationships, sub-elements, values,variables, properties within the candidate micro-domain; (3) Refine thescope of the candidate micro-domain given the newly understoodrelationships, sub-elements, values, variables and properties; and (4)Determine whether the candidate micro-domain meets criteria to bespecified as a true occurrence of the micro-domain type considered.

Once micro-domains are established as objects within an object stream,the micro-domains themselves become available to be aggregated toestablish scope. In other words, the scope of a micro-domain may bedefined as a set of durations (and/or points) along a time series, theaggregation of other micro-domains or the combination of the two. In oneembodiment this 4-step process provides symmetry of scale across levelsof the analysis by which the patient safety processor uses the sameapproach of analysis at each level of pre-objectified and objectifieddata—starting with raw pre-objectified time series and going up.

Alternatively, the data from the objectified time series matrix may beconvened into images, such as moving pictures. In one embodiment, datafrom the electronic medical records and patient monitors are used togenerate graphical displays, which may include moving pictures of thepatient condition. In an embodiment, such moving pictures, or animateddisplays, may be referred to as “motion pictures of physiologiccondition” (MPPC). Provided herein is a processing system and method forgenerating real-time MPPC of clinical data. The data and/or images mayalso be analyzed to detect perturbations, aggregate and cascadingperturbations, perturbation relationships, physiologic responses toperturbations, treatments associated with the perturbations, physiologicresponses to the treatments, physiologic failures, testing failures,treatment failures, and communication failures to generate the MPPC. Inaddition, the MPPC may also include a graphical representation of anytreatment applied in association with the clinical condition.

Once the image or moving image (i.e. an image that includes more dataover time as the patient monitoring progresses) MPPC of the patientcondition has been generated, this image may be further processed tocreate an operator-interpretable indicator to assist in patientdiagnosis and/or treatment. For example, the image may be directlycompared to a database of similar images taken from patients withclinically confirmed diagnoses. The database image or composite ofmultiple images with the greatest similarity to the generated image mayindicate the correct diagnosis for the patient. For example, if thegenerated moving image, particularly as the image progresses over time,has the greatest similarity to a database image indicating, for example,“septic shock cascade”, “inflammation failure”, “pulmonarythromboembolic cascade”, “hemorrhagic failure cascade” to name a few, aprocessor may generate a text or other indicator to a healthcareprovider indicating such a diagnosis. The processor may also indicatethat additional tests should be ordered to confirm the diagnosis. Theprocessor may also indicate and/or provide orders for specifictreatments in light of the diagnosis. In an embodiment, a moving imagemay be indicative of two or more clinical conditions. The processor mayindicate tests that may rule out one or more of such conditions. Inaddition, over time, one condition may be determined by the processor tobe more likely while additional time-series data may also rule outanother condition.

These database images may be formed from retrospective clinical data. Inan embodiment, the images may be analyzed for similarity by any suitabletechnique, including image registration. In embodiments, the matches maybe made by image similarity measures that include cross-correlation,mutual information, sum of squared intensity differences, and ratioimage uniformity. In an embodiment, the individual time-series objectsthat make up the image may be processed as a group for similarity toother groups of time-series objects associated with a particulardiagnosis or clinical condition. The Motion Picture of PhysiologicCondition (MPPC) may, for example, include abnormal and/or perturbedcomponents and in particular “Motion Pictures of Physiologic Failure”(MPPF) of the physiologic system and of exogenous forces relating tothat system. Provided herein is a processing system and method forgenerating real-time MPPCs of healthcare signals and processing thoseimages to timely detect perturbations, aggregate and cascadingperturbations, perturbation relationships, physiologic responses toperturbations, treatments associated with the perturbations, physiologicresponses to the treatments, physiologic failures, testing failures,treatment failures, and communication failures to generate and thenrecognize motion pictures of physiologic failures and of the treatmentapplied in association with the failures.

Also provided herein is a processor and processing method for theautomatic generation and/or analysis of the images of physiologic and/orclinical condition and the characterization and aggregation of the imagecomponents of complex dynamic systems, such as physiologic systems andmedical care systems. The processing system may generate real-time MPPCof healthcare signals and processing those images to timely detectperturbations, aggregate and cascading perturbations, perturbationrelationships, physiologic responses to perturbations, treatmentsassociated with the perturbations, physiologic responses to thetreatments, physiologic failures, testing failures, treatment failures,and communication failures to generate and then recognize motionpictures of physiologic failures and of the treatment applied inassociation with the failures. According to one embodiment, a processorfirst renders parallel time-series from each of a plurality of sensorsand testing sources, which are applied to broadly monitor the dynamicsystem for failure. In an example, a processor programmed withinstructions for time series objectification of patient data detectspatterns along the parallel times-series, converts these patterns intotime series of discrete objects, then organizes these objects intodiscrete relational objects (such as binary objects, or relationalbinaries, derived of relational object pairs). The processor thenorganizes the relational binaries to render a unifying programmaticimage of the physiologic system and the care provided. The processorthen automatically recognizes objects in the image components and may beable to perform analysis on the images.

One embodiment may include a patient safety processor having a singleprocessor or a combination of processors programmed to generate timeseries objects, a relational binaries, moving images, patient safetyimages, and/or patient safety visualizations. The patient safetyprocessor outputs images of the patient's physiologic system and medicalcare. In an embodiment, the processor includes processing functions fortime series objectification, relational binary processing, and animaging processing. In an alternative embodiment, the patient safetyprocessor combines multiple processing mechanisms (e.g., time seriesobjectification, relational binary processing, and imaging processing)into a single matrix construction processor.

According to an embodiment, perturbations detected by the processor areconverted to image components that may be used to generate a movingimage. In an embodiment, an MPPC may be representative of a “motionpicture of physiological failure” (MPPF) when a failure image becomesprogressively more complete and recognizable by the processor as eachadditional failure image component is added. One embodiment may involvebuilding a dynamic real-time image of disease, injury, and/or drugreactions, the care provided, and the expense associated with that care.The image is initially associated with initial image componentsincluding one or more minor perturbations, which may for example becaused by circulation of one or more toxic, acidic, and/or immunogenicmaterial of endogenous or exogenous origin. At first theseperturbations, such as toxins, inflammatory and/or thrombogenicmediators, may induce and/or cause only minor changes in cellpermeability, ion flux, or hydrogen ion elevation, and trigger variousminor physiologic perturbations and responses each of which may producean image component. The measurements of various mediators, ions,biologic profiles, as well as standard blood tests, and the outputs ofvital sign monitors may begin to vary as a function of these earlyphysiologic perturbations and responses, and it is these variations thatenlarge the group of image components from which the larger image isderived. Early in the process, each of these alterations inpermeability, cell injury, mediator production, and physiologicperturbations, when considered in isolation, are often minor. However,collectively they may represent the early manifestations of a nascentand evolving moving image of a serious clinical condition.

According to one embodiment, each perturbation is programmaticallyorganized to form an image component of the MPPC. Many of these detectedimages components may be isolated because they are related to a benignprocess, and the image may self-extinguish or may not develop into animage associated with a clinical condition involving intervention or anMPPC. Yet, as noted above, others may represent the first imagecomponents of an early moving image. Provided herein are systems andmethods for the detection of the early image components of an evolvingmoving image to provide timely detection of physiologic failures beforethese failure progresses to shock (including, for example, hypovolemic,obstructive, septic, toxic, cardiogenic, hypoxic, and/or hypercarbicshock.) In one embodiment, it is advantageous to detect the early imagecomponents of the moving image before shock develops to improve theprognosis for the patient and to apply goal-directed therapy whileclinical intervention is still beneficial.

According to one embodiment, a patient safety processor constructs aprogrammatic MPPC, which is used for dynamic, motion picture responsive,protocolization of care. This motion picture is comprehensive, includingnot only the events including a single or few parameters such as a theheart rate, but also other parameters that may include, for example: theslope and pattern of the heart rate, the slope and patterns of thesystolic pressure variation, the slope and patterns respiration rate,the slope and patterns SPO₂, the slope and patterns ventilation-oximetryindex, the slope and patterns drug and fluid infusion rate, the slopeand patterns blood pressure, the slope and patterns of the Neutrophilcount, and the slope and patterns of inflammatory and/or thromboticmarkers, and various other blood, urine and/or exhaled gas test to namea few. The signals from all of these sources may be converted totime-series and may, for example, be physiologic signals, therapysignals, laboratory signals, or historical signals, which may beobjectified, as by an objectification processor, to produce the discreteprogrammatic objects (events). According to one embodiment, theprocessor detects a first discrete event that includes a pattern orvalue of at least one medical signal, and a second discrete event thatincludes a second pattern or value of at least one medical signal, theprocessor then aggregates at least the first event and the second eventto produce a first relational object, the processor further detects athird event that includes a pattern or value of at least one medicalsignal, and a fourth event that includes a second pattern or value of atleast one medical signal, the processor then aggregates at least thethird event and the fourth event to produce a second relational object.The first relational object and the second relational object are thenaggregated to produce a first image component. Additional images arebuilt accordingly and the image components are then aggregated accordingto the time of occurrence to derive the moving image and care.

In an example, the pulse related components of the typical motionpicture of sepsis failure cascade would include occurrences such asearly rise in heart rate, rise in pulse amplitude, and rise in slope ofthe pulse upstroke (as measured at the finger tip) in combination andtypically proceeded by a brisk rise in inflammatory markers. In contrastthe typical motion picture of occult hemorrhagic failure cascade (as forexample due to heparin related retroperitoneal hemorrhage) would includeoccurrences of an early rise in heart rate, a fall in pulse amplitude,and a fall in slope of the pulse upstroke (as measured at the fingertip) and a rise in the respiratory related pulse pressure variation anda fall in hemoglobin. According to one embodiment, all of theseoccurrences along the image of an occult hemorrhagic failure cascade canall be derived from a multi wavelength pulse oximeter.

According to an embodiment, a relational binary processor is providedthat divides detected variations into discrete alpha events and betaevents, which are combined by the relational binary processor toconstruct the relational events which are termed relational binaries.These relational binaries are aggregated according to timing, frequency,and/or spatial relationship to construct images. These images are thenfurther aggregated according to timing, frequency and/or spatialrelationship to construct and progressively build MPPC (from whichvisual images or electronic representations may be derived as desired).These MPPC are often moving images of catastrophic cascading failures,thereby allowing more reliable detection to allow timely rescue of thepatient.

The signals may be chemical or physiologic measurements, as provided bypatient monitors, recorded in the electronic medical record, and/or maybe biomarkers specifically ordered, either automatically by theprocessor or manually by the clinician to indicate the potentialpresence of the sepsis (as those, for example, disclosed in U.S. patentapplication Ser. Nos. 10/704,899, 11/647,689). The presence and/orconcentration of such markers may be presented in the context of theMPPC with the timed positioning relative to the others parameters, whichthen allows the relevance of the biomarker to be much more readilyidentified. According to an embodiment, the temporal and relationalpattern of inflammatory markers and temporal and relational patterns ofcontemporaneously measured or associated physiologic parameters areaggregated to produce a progressively enlarging MPPC of an evolvingpatient condition.

Therefore, to achieve the detection of various pre-shock states as wellas earlier detection of failures, one embodiment detects earlyvariations and aggregates them to provide an MPPC to dynamically presentexpanding failure cascades of pre-shock and shock states. This allowsseparation of expanding images from the smaller and less expansive imagecomponents having benign characteristics, and further allows separationof the images of minor isolated failures from failures that progress togenerate an expanding MPPC heralding the potential for transition to oneof the shock states. Each group of images as well as the complete MPPCand care may be analyzed for the purpose of assessing patient care in ahospital, a ward, or under the care of a given healthcare worker.

The occurrence of a large number of images indicating non cascadingfailures which self extinguish may be indicative of an unstable patientpopulation or poor health care delivery. In the alternative, a largenumber of cascading failures are indicative of risk of injury. The MPPCand the images may be used to determine if that is due to the patientpopulation or the quality of the care.

One embodiment detects failure cascades along with the determination ofthe specific fundamental perturbations, or treatments, or lack oftreatments that occur early in a failure cascade. Specific fundamentalfailures are detected before they progresses to complex failures andparticularly before they progresses to the pre-shock or shock state.Furthermore, the processor builds an image derived of the relationalperturbations and treatments as the cascade expands. According to oneembodiment, each time series is processed to separate expected eventsfrom unexpected events. The unexpected and/or abnormal events are thenaggregated further to repetitively generate relational events, imagesand finally the MPPC which includes a motion picture of the cascade (ifpresent) as well as the treatment applied in association with thecascade. This MPPC is further processed to allow the detection of theprobable cause or causes of the occurrence of the moving failure imageswell as the images of the MPPC as it evolves thereby allowing detectionof the nature and cause of the failure cascade.

As noted above according to one embodiment, an analysis is providedwherein the fundamental components of the analytic process include abasic relational variable that includes a plurality of events. In acontemplated embodiment, the basic relational variable is that includestwo events (a relational pair) and this is called a relational binary.In one embodiment, the relational binaries are initially selected by theusers as from a menu (or by a drag and drop interface) of relationalbinaries and/or of events from which the user builds the desired objectbinaries the binaries are then used as by drag and drop to build thedefinition of images for detection. This may be performed by, forexample, by national or regional expert groups, or by specificdepartments in a hospital, or by an individual physician to providecustom management. This may also be automatically performed by theprocessor (as, for example, through the investigation of a large numberof historical data sets that have been comprehensively analyzed andcategorized according to outcomes. The objectified time series matrixand/or the MPPC may be may be outputted in various interactive,hierarchical, and relational formats for review and automatic or manualadjustment. The MPPC may detect a wide range of failures, such asphysiologic failures, treatment occurrence failures indicating theabsence of expected treatment in relation to a given perturbation,testing occurrence failures indicating the absence of expected testingin relation to a given perturbation, treatment response failuresindicating the absence of the expected correction of perturbation or theoccurrence of a new potentially complicating perturbation in relation toa given treatment and/or dose.

The processor combines the complex data of the electronic medical recordinto a single motion picture of perturbations, treatments, physiologicresponses, diagnostic testing, recoveries, diagnoses, missing data,patient locations, and/or other datasets. Dynamic images are generatedof relational variations of a set of time series associated with acomplex system to generate a real time motion picture of a failure ofthe system and/or of forces applied to the system. According to oneembodiment, the patient safety processor automatically outputs a unifiedtimeline, for example, derived of detected images of a given type.According to another embodiment, the processor, upon detecting a failurecascade, may present and highlight the evolving MPPC in real time on anoutputted display of an image diagram for the physician to review. Theportion of the motion picture, which has already been completed, may bereviewed backward and forward to review in a single summary snap shotview.

Many physiologic failures such as, for example septic shock, pulmonaryembolism, congestive heart failure, respiratory arrest due to narcoticsin the presence of sleep apnea, thrombotic thrombocytopenia purpura(TTP), hemorrhage due to anticoagulation, respiratory failure due tobronchospasm, and adult respiratory distress syndrome, but not limitedto these clinical conditions, begin with one or two non-specificperturbation(s). Physiologic failure is commonly a relational expansion,often beginning with a fundamental physiologic perturbation at a singlefocal point in time. In fact, this initial perturbation is oftencompletely masked once the cascade has progressed past a certain point.In such cases, testing or monitoring for the single perturbation may notbe useful for making a diagnosis. In many cascading clinical conditions,the first perturbation(s) of the cascade may often only be detected inretrospect after the cascade has further progressed when the firstperturbation(s) is no longer present. This provides a basis foroptimizing the detection of the first point(s) by real-time imaging ofthe cascade as it develops and then examining the image to determine thefirst perturbation(s).

While a pattern of a single time series provides a larger image of adynamic process than a single value or range, such a pattern is stillonly a tiny image fragment of the process. The determination ofthresholds and even the detection of various patterns of perturbationsinclude incomplete analysis, which will inevitably allow an unacceptablerate of progression to catastrophic failure. Even in situations whereina measurement or test may seem definitive as a stand-alone test, actionor conclusions based on a single value (or an average of a plurality ofvalues) will have a reasonable probability of being incorrect. Consider,for example, a single measured spot SPO₂ value of 94. This value islargely meaningless without knowing if the SPO₂ is rising, falling, orcycling. Yet this infinitesimal image fragment of a patient's complexphysiologic system is used everyday in hospitals to determine care.Furthermore, even if the pattern of the SPO₂ is known (for example theSPO₂ has been stable at about 94 for at least 12 hours) this is anincomplete image, which is largely useless and, in fact, a potentiallymisleading piece of information. Without knowing the relational patternof the minute ventilation during the related time interval of themeasured SPO₂ pattern, the healthcare worker may be lulled into a falsesense of security even as the patient is dying of septic shock or heartfailure. Furthermore, an alarm or interpretive output which is based ona programmatic image of both the patterns of both the SPO₂ and therelated minute ventilation without additional relational elements of theimage, such as, for example, the associated pattern of the white bloodcell count, temperature, pulse, blood pressure, microbiologic values,and medications will be incomplete leaving too much synthesis for thehealthcare worker. In another example, consider the detection of apattern of a sustained rise in pulse or respiration rate. Each suchpattern represents a tiny fragment of the present physiologic state andeach pattern may be benign or alternatively may be an early imagecomponent of a much larger dynamic process of failure often associatedwith an evolving failure cascade. The difference between a benign orpathologic rise in pulse or respiration rate cannot be determined withthis tiny image alone and often cannot even be known at the time of theonset of the rise. Therefore a tree diagram protocol with a branch basedon a rising pulse or rising respiration rate adds a great degree ofprogrammatic complexity with a high risk that the protocol will precededown the wrong pathway. An incomplete analysis of the physiologic systemwill often cause the healthcare worker to generate a large amount ofinvestigation, testing, analysis and evaluation that is not necessaryand therefore increases the cost of overall care. Further, these falsepaths of treatment and evaluation may distract the care worker from thedetermining the actual operative failure modes.

Prior to shock, a patient's physiologic system is perturbed by bothdisease and treatment. A given treatment provided to correct aperturbation might reduce the perturbation, have no effect on theperturbation, exacerbate the perturbation, cause another perturbationand/or make another perturbation worse or better. To determine whicheffect a treatment is having and to assure that this determination oftreatment effect is complete, it is necessary to collect and, just asimportantly, as provided by one embodiment, organize and analyze largeamounts of relational data in a timely manner.

Another problem is that, within present hospital systems the healthcareworker is forced to do a great deal of archeology (digging, isolating,identifying, etc.) before synthesis may be effectively completed. Forthis reason, the synthesis of information by the healthcare worker isoften not executed in a manner, which allows immediate searching,filtering, re-analysis, etc. This friction combined with the typicalworkload of healthcare workers limits the number and range of high-levelscenarios, which may be investigated. Also the healthcare worker may,because of lack of available organized data and time, execute decisionswithout a complete set of synthesized information and worse, may notrealize that this is the case.

For these reasons, even with conventional electronic medical recordembedded protocols, patients remain subject to a range of failuresacross a broad range of failure modes based on the complexity of theirindividual condition and the complexity of the environment facing thecare giver. In fact, because failures often overlap, one protocol mayreduce the risk of one failure while increasing the risk of another. Forexample, oxygen given to treat hypoxemia under one protocol may delaythe detection of pulmonary embolism by stabilizing the SPO₂ and hidingthe early signs of impending shock from the healthcare worker.

Because so many confounding and overlapping occurrences can be present,the time series objectification processor, the relational binaryprocessor and imaging processor execute multiple iterations of analysisand refinement. In one embodiment this analysis would begins with aphase one execution in which each processor in order (time series,relational binary, and imaging) operates on the specified set of timeseries inputs storing the interim analysis results in memory and/or inthe patient safety image database. After this has been completed, thepatient safety processor may execute phase two preferably in the sameorder providing each processor with the original time series data aswell as the full analysis from the previous phase(s). This second phasemay refine the analysis in terms of the first phase analysis. Thisprocess may contain as many phases as required for complete refinementof the analysis.

In one embodiment, each phase uses the same definition sets (eventdefinition set, binary definition set, image definition set), butdetermines which property evaluations, rules and constraints areavailable per phase. The order of time series is determined to maximizethe availability of constraints. If a rule and/or constraint cannot beenforced in the current phase then it is ignored allowing for a relaxedset of rules to be executed and a greater number of objects to beidentified. With each subsequent phase additional rules and constraintsare applied as they become enforceable until all rules and constraintshave been applied and all properties have been calculated, evaluated andassigned.

As an example, if an oximeter supplies a time series for oximetry andmotion artifact, the multiphase analysis will allow the rule to rejectan event on the existence of motion artifact unless the motion artifactis determined to present a particular density, pattern, frequency,magnitude, and/or have a relationship (as for example occurring atsubstantially the same time) with an arousal or other event or image.The definition of an arousal (an image) is created in terms of oximetryrelational binaries and pulse events. Since the definition of anoximetry event depends on the existence of a higher-level object ofwhich it will be an element, the patient safety processor recognizesthat a single-phase approach will be inadequate and sets up a two-phaseapproach. Within the first phase the times series objectificationprocessor creates oximetry events without the artifact constraint andthe relational binary processor and image processor execute with theexpanded set of oximetry events. In phase two, the results of phase oneare available to the processors and the times series objectificationprocessor may evaluate the complete rule.

If the processor determines that motion artifact of a particulardensity, pattern, frequency, and/or magnitude exists in relation to thepoint of time in which an oximetry event would be created the processormay query the phase one analysis for the given time window to determinewhether an arousal exists. If an arousal does exist, then the motionartifact may be eliminated as rejecting the event and the analysis maycontinue as before. If an arousal does not exist, then the oximetryevent may be rejected and, as the analysis continues, any higher-levelobjects that were dependent on the existence of that event will fail tobe created. In one example, a micro-arousal (an image) may be defined asoccurring when a new onset of motion occurs at a time when motion is notdense and near the end of a desaturation event and wherein the motion istemporally associated with a recovery from the desaturation. In additionthe image of the micro-arousal may include a positive reciprocation ofthe in slope of the ascending portion of the pulse waveform, a positivereciprocation of the heart rate, a positive reciprocation of the pulseamplitude, to name a few events, binaries and/or images which may beincluded.

In an alternate embodiment, each phase has a specific group ofdefinition sets (event definition set, binary definition set, and imagedefinition set), which are constrained by the inputs of the phase.Within the multiphase approach of analysis the definition of events,relational binaries and images may be defined in terms of a related timeseries (e.g. an artifact indication stream), a derived/transformed timeseries (e.g. a time series derived from a calculation from two or moretime series) and/or higher-level elements from a previous phase. In analternative embodiment, post-processing phases are designed to refineresultant analysis. In this embodiment, rules are applied after theanalysis to alter elements if they meet certain criteria. The patientsafety processor would be supplied with a post-analysis rule set foridentifying elements that should be added, altered or deleted. Using theabove example, the event definition set used by the time seriesobjectification processor will not include the constraint with referenceto the motion artifact. Rather, the post-analysis rule set would includethe rule to remove all events if motion artifact is found at the sametime and the event is not part of an arousal. The post-processingexecution would look for all events that meet this rule and mark themfor removal. After all post-processing criteria have been evaluated thepatient safety processor would begin the process of analysis alteration.In this embodiment, as well as other embodiments, each object providesthe functionality to determine all objects upon which it is dependentdown to the event level. In one embodiment, this functionality isaccomplished through a recursive database procedure. Once all dependentelements are identified, each will be evaluated as to the action totake. For example, a relational binary will be removed if an event isremoved, but a cycling relational binary may or may not be removed withthe removal of a single event.

In an alternative embodiment, the construction of the objectified timeseries matrix is accomplished by a single matrix construction processorconsuming two inputs—a set of raw time series and an occurrencedefinition set. In this embodiment, occurrence definition, includingsub-elements of definitions, would contain explicit dependencyrequirements for construction. Dependencies would be defined in terms ofraw time series required, occurrence stream required, and/or thespecific sub-elements within the specified time series and/or occurrencestreams. Each occurrence definition would include a potential occurrencestream which may be added to the matrix. The matrix constructionprocessor would examine each occurrence definition iteratively and insuccession to determine whether the dependencies for the specifiedoccurrence are available for analysis. If all dependencies are availablethen the matrix construction processor executes the necessary analysisto identify, qualify and completely construct all occurrences associatedwith the given occurrence definition. The resultant occurrences, if any,are aggregated into an occurrence stream and added into the matrix.

For example, an oxygen rise event may be defined as an occurrence with adependency only on the existence of a raw oximetry time series. In thiscase, the matrix construction processor would be free to construct theoxygen rise occurrence stream and place it into the matrix as soon as araw oximetry time series is added to the matrix. Once the oxygen riseoccurrence stream has been placed into the matrix any occurrencedefinitions that are dependent on the oxygen rise occurrence (forexample the oxygen reciprocation occurrence) may become available forconstruction. To provide additional flexibility within this embodiment,construction may be broken into the 4 stages described above: (1) typeand Scope establishment; (2) property, relationship and sub-elementcreation; (3) Scope refinement; and (4) Occurrence qualification.

During the first stage a candidate occurrence is created. In oneexemplary embodiment candidate occurrences are aggregated into stream sand placed into the matrix. Occurrence are not marked as qualified (e.g.true) occurrences until stage 4 of construction has been completed forthe given occurrence definition. Construction may be interrupted betweenand within the stages as described above. If the dependencies requiredto accomplish stage 1 is available but the dependencies for stage 2 arenot available, the matrix construction processor will proceed with stage1 of construction but leave the subsequent stages for later processing.In one embodiment, candidate occurrences may be used within stage 1construction of other occurrence definitions. In this embodiment, thematrix construction processor identifies all occurrences as being in oneof four possible states: candidate, qualified, disqualified and suspect.Candidate occurrences are occurrences that have completed stage 1 butnot stage 4 of construction. Qualified occurrences are occurrences thathave met all the requirements specified in stage 4 of construction.Disqualified occurrences passed stage 1 but either explicitly failedstage 4 of construction or was one of its required sub-elements or scopeobjects was later determined to be disqualified or altered in such a wayto fail scope requirements and/or qualification. Finally, suspectoccurrences are occurrences that have passed stage 1 but one or more ofits dependent sub-elements or scope objects has been determined to bedisqualified or altered in such a way that further analysis may revealit will fail scope requirements and/or qualification. The inclusion ofthe suspect state allows for flexible analysis execution in cases wherecandidate occurrences have been used by other occurrences to establishscope. In the case that the candidate occurrence is found to bedisqualified, the matrix construction processor marks all occurrenceswhich were dependent on the candidate occurrence as suspect. Suspectoccurrences may be subsequently analyzed to determine whether thedisqualification of the associated candidate occurrence (or alterationof a property on which the parent depended) does in fact disqualify theparent occurrence. This embodiment has several advantages, including themaintenance of a complete set of dependencies for occurrences,occurrence properties, relationships and sub-elements provides foradditional information within the patient safety console. For example,if the patient safety console displays an index (for example, aninstability index), the availability of the dependency tree allows theuser to examine what elements go into the evaluation of the index andfrom which properties, relationships and sub-elements they were derived.In addition, evolutionary states of analysis may be persisted andanalyzed by researchers and/or the patient safety processor to furtherunderstand the relationships within the resultant MPPC. Disqualifiedoccurrences may be evaluated for research purposes and especially duringthe construction of occurrence definitions. Disqualified occurrences maybe identified as “near misses” such that the researcher or the automatedprocess within the patient safety processor may evaluate the result ofchanges within the occurrence definitions. The independent and iterativenature of the analysis approach along with the persistence of interimstates lends itself to parallel, concurrent and/or distributedprocessing of the matrix. In other words, matrix construction may beexecuted within multiple threads of execution on multiple processorswithin a single machine or by any number of independent machines.

As discussed, according to one embodiment, the relational binaryprocessor generates relational binaries. Such relational binaries areincluding an alpha occurrence object and a beta occurrence object. Anearly step in this process includes the defining the relational binariesby the user or by the processor. To define a relational binary, first,the alpha occurrence may be defined (as by the user or adaptively). Thealpha occurrence may be, for example, an event defined both in terms ofits channel and the object along the channel. In a contemplatedembodiment, the objects along each channel are defined bycharacteristics (such as the slope, amplitude, or other featuresdefining the object including as discussed in the aforementioned patentapplications). As well, a beta occurrence defined as, for example, anevent in terms of its channel and its characteristics. Alpha and betaoccurrences may be events, other relational binaries, images, repeatingimages or pattern images to name a few.

The definition of events within the patient safety processor depends onthe time series mode and type. The patient safety processor supports twotime series modes: numeric and non-numeric. For each mode, there areseveral time series types. For the numeric mode, the patient safetyprocessor supports several types that specify the type of data pointthat may be stored within the time series. Numeric types includeinteger, floating point, double precision, decimal, positive integers toname a few. Non-numeric types include Boolean, domain and freeformstring to name a few. With numeric time series directional events may bedefined in terms of characteristics of the segment including magnitude,duration, and slope to name a few. Threshold violations also may beidentified as an event. With non-numeric types events may be defined aseither a state matching event or a state transition event, directionalevents may also be defined in terms of best-fit or least-squared linearregression approach, by an imaging approach, a polarity definingapproach, to name a few.

With state matching events a set of values may be defined (either by theidentification of individual values or by a pattern-matching mechanismsuch as a regular expression) and optionally a minimum and maximumduration may be selected. An event may be said to have occurred if andwhen and for the length of time that the “points” within the time seriesmatch the target set for at least the specified minimum duration (ifany) and for no greater than the specified maximum duration (if any).

State transition events may be defined by selecting two sets of values(either by the identification of individual values or by apattern-matching mechanism such as a regular expression). An event maybe said to have occurred if and when a first “point” is in the first setdefined and the next subsequent point is in the second set defined. Thepatient safety processor provides for a unary “not” operator to handletwo important specific cases of state transition. The researcher maydefine the first set specifically and then define the second set asbeing “not” the first set. In this way, an event may be created when a“point” is in the first set and the next subsequent point is in not inthe first set. This is a specific type of state transition called aleaving state event. Similarly the second set may be specificallydefined and the first set may be defined as “not” the second set. Inthis way, an event may be created when a “point” is not in the secondset and the next subsequent point is in the second set. This is aspecific type of state transition called an entering state event.

Alternatively, state transition events may be defined by specifying astate flow diagram or state machine definition and the beginning and endstate for the transition. In this type of event, the transitional statesare maintained as part of the state transition event as well as theorder in which they occurred. Events may be individually defined with astatistical approach rather than an absolute or relative approach withany or all of the characteristics used for the definition. For example,a directional event may be defined as a deviation from previous (orsubsequent) trends within a selected window of time. The comparison setmay be the stream within which the event potentially exists or it may bea designated group of stream s (e.g. a group designated as “normal”), arandomly selected group or the entire set of stream s of the specifiedtype available. This deviation may only consider sets already analyzedand sections only before a fixed or moving point in time (as in a realtime analysis or simulated real time analysis) or may use all sets andtimes available. In an alternative embodiment, the search for deviationmay be applied with reference to a definitive object or set ofdefinitive objects selected by the processor or by an expert.

Both alpha and beta occurrences may also be defined in terms of therelationship of its characteristics to the characteristics of thecandidate correlating occurrence (i.e. the alpha for the beta or thebeta for the alpha). In one embodiment the user may define therelational objects, (as by using a drag and drop designer), by selectingthe channel or stream (which defines the time series type), and byselecting the occurrences which meet specified range of criteria, and byidentifying the timed relationship (such as the time interval) of thebeta occurrence in relation to at least a portion of the preceding alphaoccurrence, and/or by identifying the spatial relationships and/orfrequency relationships of one occurrence to the other occurrence, mayIn the most fundamental relational binary, the event binary, the alphaand beta occurrences are events identified by the time seriesobjectification processor, for example such as a time seriesobjectification processor in U.S. patent application Ser. No. 11/280,559and U.S. Pat. No. 7,081,095, the disclosures of which are herebyincorporated by reference in their entirety for all purposes as ifcompletely disclosed herein. The relational binary processor thenaggregates the relational binaries according to their time of occurrenceand/or to specific criteria for aggregation set by the user or processorto derive images and the images are aggregated according to their timeof occurrence to derive the MPPC and care derived of events and patternsacross hundreds of parallel time series. In a sense, the relationalbinaries and events become the discrete “pixels” from which MPPC of apatient's physiologic system are constructed by the patient safetyprocessor.

According to one embodiment, the patient safety processor may be alsoprogrammed to organize the events and relational binaries into largeraggregate factorable objects, which may also be constructed as a unifiedobject timeline rather than a motion picture. Each aggregate factorableobject may include a specific aggregation of events and relationalbinaries objects. In some aggregate factorable objects, the individualrelational binary and event objects occur in a specific sequence orrange of sequences (which may be overlapping) and the objects have aspecific temporal relationship (or range of temporal relationships) withrespect to each other. One specific type of object timeline may bespecified as simply a grouped set. In another example, relationalbinaries are ordered in specified sequence in which the event andrelational binaries objects were detected thereby defining the objecttimeline.

According to one embodiment, objects of specified types may also becombined derived to render a “unified patient timeline” which may be asimple summary of the patient's physiologic system and care. The MPPCand care provides the information at more comprehensive level. Both maybe configured to provide further simplified summarization or imagedetail revealing drill down. The unified patient timeline may forexample, represents an instance of at least one factorable aggregateobject derived from a plurality of parallel time series into a singletime-series or time line, often of relational binary objects of aspecific type or plurality of types. In one instance the unified patienttimeline and/or the MPPC and care may be constructed to be a life longtime line and/or motion picture, which preferably may be recordedwhenever signals are available, such as during a hospitalization or whenconnected to a home monitor or when blood testing is made. The beginningof the motion picture or time line may be defined by the time of theearliest date of data (which may be derived from archived patient data)the unified patient timeline does not end until a patient dies, segmentsof the timeline (or motion picture) may be separated for examination bylocation of the patient such as a hospitalization segment, or by actionstaken to treat the patient, such as a peri-operative segment, or byevents relating to altered patients states such as the segmentimmediately preceding death or while sleeping. According to oneembodiment, an object nomenclature may be provided which designates thetimed and sequence relationships of the binary objects and events of aplurality the parallel patient related time series, thereby converting alarge plurality of datasets into this single time series of factorableobjects, which may be readily outputted interpretable throughapplication of a succinct nomenclature.

In one embodiment, the physician may mark a test result or other datapoint as mistaken or anomalous. In this case the processor splits theanalysis into two—the working analysis (which removes or alters the testresult or other data point) and a background analysis (which maintainsthe original data). The processor may run scenarios in which theoriginal test result stays in effect to determine if conditions occurthat might have been expected from the “so-called” anomalous test. Thebackground will not affect the working analysis but notification may begenerated if a correlation of events is found in a sufficientlysuggestive pattern to warrant a consideration that the original testresults may not have been mistaken and, in fact, would account forconditions that do not fit the current working state (e.g. the statewith the test results removed). Background analyses may be deletedaccording to time (e.g. after a certain amount of time in which nocorrelation to following events is found) or at the request of the useror system operator (e.g. to reduce resource requirements).

In another example the processor may be programmed to generate morefrequent testing binaries to confirm or exclude an apparently evolvingimage. In this way the processor is trying to look as far forward aspossible with additional testing to confirm the motion picture of aparticular failure as early as possible so that the delay associatedwith waiting for the detection of a failure cascade as by varioustraditional threshold breaches is eliminated.

In an example, as part of assuring that the future image is complete,the testing binaries are designated such that the addition of certaindrugs (the alpha event) into the image, may cause automatic orders fortesting to monitor for complications related to the drug (the betaevent) if selected events, binaries, and/or images are present. In anexample, if the physician orders heparin, a testing binary may begenerated and added to the image, which includes automatic order for aplatelet count every 48 hours. According to one embodiment, the timeseries objectification processor is objectifying the time series ofplatelet counts to detect a least one fall event (as for example definedby a negative slope and/or a magnitude of fall and/or a threshold fall),if a fall event is detected a divergent binary is generated and a markerindicating a fall is added to the image along the platelet count timeseries, the processor may generate more frequent platelet testingbinaries, to confirm the presence of these divergent binaries in theimage. If multiple divergent binaries are detected then the processormay generate different types of testing binaries wherein the alpha eventis the fall in platelet count. This may trigger a cascade of testingbinaries such as, for example, wherein the alpha event is a binaryincluding a heparin treatment and a fall in platelet count and the betaevent is, for example, a platelet factor IV assay and/or another assay.In this way, using the imaging processor, the delay associated withwaiting for an absolute or relative threshold drop in the platelet countis not required but rather the slope of the platelet count, the presenceor absence of prior heparin therapy, the patient's risk of bleeding orthrombosis, may all be included in the image to trigger the automaticmeasurement of additional testing. In addition the image of cascade asit evolves may trigger additional testing binaries (as for hepaticfunction tests, as required to determine the safety of Argatoban, amedication which may be ordered if the images are consistent withheparin induced thrombocytopenia). Here the advantage of having thesebinaries and images as part of a MPPCF is evident, because the processorwill be examining the images of the motion picture for other causes ofthe fall in platelet count which may include cascades indicating TTP aswill be discussed and/or occult hemorrhage to name a few.

One embodiment programmatically images the parallel physiologic timeseries to render a relational pyramid of data with the top of thepyramid representing data at the highest level of analysis andabstraction while data moves down through layers of analysis, the bottomlayer being the raw data stream s. The healthcare worker may investigatethe pyramid in the following ways to name a few: (1) Drilldown—the careworker may navigate into the details of the data and the rationale ofthe analysis (i.e. both the conditions that exist and the rules by whichthe analysis has arrived at its conclusion); and (2) Aspects—view portsinto the system which emphasize certain elements/conditions andde-emphasize (and/or filter out) other elements/conditions). These twoexamples above may be used together allowing the healthcare worker tonavigate through the relational pyramid vertically (drilldown throughlevels of analysis) and horizontally (through filters/aspects).

In one embodiment the relational pyramid may be manipulated by thehealthcare worker and/or researcher to consider hypothetical scenariosor scenarios based on the rejection of certain test results or eventswhich may be considered in error, anomalous or otherwise inaccurate.Alternate pyramids may be stored in whole or as differential images.Alternate pyramids may be compared against the working pyramid tounderstand the results of the altered data.

In one embodiment, the processor will automatically consider alternatepyramids under certain conditions—such as the existence of perturbationfor which no precursors may be identified. The sudden existence ofperturbation or of divergence may, by considering the range of possibleprecursors, suggest anomalous conditions: inaccurate diagnosis, faultymonitoring equipment, labeling mistakes, the failure of a patient totake medication as prescribed, to name a few. According to one aspect,the values and/or patterns of the blood tests such as the inflammatorymediators is/are compared to the image(s) of physiologic perturbation orto the pattern(s) or values of at least one physiologic parameter, suchas the pulse rate, respiration rate, and/or ventilation oximetry indexto name a few. Upon the detection of an apparent relationship, theprocessor may automatically order a sufficing number of sequential bloodtests to confirm that the pattern of the parameter is convergent withthe pattern of the blood test thereby providing strong supportingevidence, reinforcing redundant evidence, that the physiologic parameterand the mediator have a common physiologic failure based linkage, suchas the failure of sepsis for example. One embodiment extends thatanalysis to incorporate specialized inflammatory mediators into themoving picture of failure so that comprehensive comparison of the markeror indicator to the image of the physiologic parameters and treatmentmay be provided. One embodiment generates dynamic images of relationalvariations of a set of time series associated with a complex system togenerate a real time motion picture of a failure of the system and/or offorces applied to the system and condenses the complex data of the EMRinto a single motion picture of perturbations, treatments, physiologicresponses, diagnostic testing, recoveries, diagnoses, missing data,patient locations, and/or other datasets and further provides treatment,and/or testing, alarms, notifications, diagnosis, and/or orders based onthe motion pictures.

One embodiment provides a system and method for programmaticcharacterization of a plurality of related complex and dynamicprocesses, which; converts patterns along a plurality of parallel timesseries derived from each of the process into discrete objects, organizesthese discrete objects into relational objects, and, organizes therelational objects to render a unifying programmatic image of a complexand dynamic process, and then, applies expert systems to automaticallyrecognize images or image portions, or the specific motion pictureswhich are indicative of at least one failure of the complex processesand/or provides a relational object image generating and processingsystem to provide characterization and quantification of physiologicsystems by generating an organized analytic construct defined by a timeseries matrix of relational objects.

Although the number of potential modes of failure is very high in anyhospital environment, the occurrence of certain modes of failure isreasonably likely under a given set of circumstances in the hospital. Afailure mode diagram illustrating common modes of failure given acombination of a group of diseases is shown in FIG. 1. The number ofpotential failures may be very large (in the hundreds) for a givenpatient in a hospital setting and the nurse or physician is oftenexpected to monitor many such patients on the floor while timelydetecting the failures such that the nurse is expected to timely detecteven a single failure from as many as a thousand failures which mayoccur among the patients under his or her care. For this reason,processor based failure imaging and detection is desirable.

FIG. 1 illustrates a complexity diagram 200 of an example of a patienton a medical hospital ward. The diagram 200 demonstrates the level ofcomplexity that may be modeled into moving images as provided herein todetermine the nature of and origin of perturbations within this level ofcomplexity. The diagram 200 is one type of failure mode diagram whichmay be constructed by an expert panel and then used according to oneembodiment to facilitate the construction of the various components themoving images provided herein, including the events, relationalbinaries, and image components. The failure image component diagram 200includes a number of overlapping diseases present for this singlepatient including diabetes 202, congestive heart failure 204, arterialfibrillation 206, stroke 208, sleep apnea 210 and sepsis 212. Thediseases may induce physiologic failures, such as a divergent rise inventilation 216, a rapid ventricular rate 218, pulmonary edema 214, andfall in oxygen saturation (hypoxemia) 222. Furthermore the treatmentsare potentially associated with medication failures such as a highthreshold breach of the partial thromboplastin time (PTT) or a lowthreshold breach of the glucose (hypoglycemia) 234. Additionally, theadministration of a treatment (for example, insulin 224, a diuretic 226,an ACE inhibitor 228, a beta blocker 230 and/or heparin 232) to apatient may lead to additional physiologic failures (for example, a fallin platelet count (thrombocytopenia) 236, the occurrence of heart block238, a fall in serum potassium (hypokalemia) 240, a fall in serum sodium(hyponatremia) 242, a fall in blood pressure (hypotension) 244. In oneembodiment, a single patient may have early high blood glucose(hyperglycemia) 215 followed by later low blood glucose (hypoglycemia)234. As shown, the interrelationship of progression of multiplediseases, the patient symptoms, and multiple treatments may lead totreatment delay 248 or confusion 220.

FIG. 2 depicts an overview of the flow of analysis for modeling complexpatient physiological condition in one embodiment. A wide range sourcesmay provide inputs to the modeling. For example, patient monitors 256,patient records 272, historical patient data 260, lab results 264 andtherapy data 268 may provide the raw data input into the analysisstream. These inputs are converted to a set of parallel time series 276.Patterns and threshold violations along this plurality of parallel timeseries identified, coalesced, synthesized and organized into discreteobjects forming object stream s 280 within each channel. These discreteobjects are analyzed to identify known relational patterns intoinstances of relational binaries 284. In one embodiment, expert systemsthen further refine the analysis by organizing and synthesizing theserelational binaries into a set of failure images 288, which as anaggregate whole make up a unified programmatic image of the complex anddynamic state of a patient and/or a patient population.

FIG. 2 depicts the flow of analysis 240 from raw data to the aggregateof images, while FIG. 3A and FIG. 3B includes some of the data stores,data flow, processors and output mechanisms within the exemplaryembodiment. FIG. 3A depicts another data flow of one embodiment. Thedata management system 300 includes a monitor 302, a patient safetyprocessor 304 that may include, for example, time series objectificationprocessor 336, relational binary processor 348, and failure imagingprocessor 360. Alternatively, processors 336, 348, and 360 orinstructions for performing the processing steps of time seriesobjectification, relational binary processing, and/or failure imageprocessing may be located on one or more additional processingcomponents in communication with processor 304 that are part of thesystem 300. The processor 304 is adapted to provide output of theanalysis to a device 306, which provides an interface for a healthcareworker. The data flow involves inputs from a wide range of sources (302,304, 308, 310, 312, 314). As shown, the inputs may be sent to aprocessor 304 that may direct further action for the patient, includingtesting orders 316, indicators to the healthcare provider that may bedisplayed on a console or device 306, and therapy orders 315.Accordingly, the healthcare worker may use the device 306 to control andoversee the entire hospitalization process. In one exemplary embodiment,the processor 304 may be used to drive the device 306. The processor 304may be adapted to constantly process all of the real-time data of all ofthe patients regardless of the status of the viewing console and toautomatically send testing orders 316 and/or therapy orders 315 based onthe analysis of the images derived from the processor 304, as will bediscussed.

The data management system 300 may include one or more processor-basedcomponents, such as general purpose or application-specific computers.In addition to the processor-based components, the data managementsystem 300 may include various memory and/or storage componentsincluding magnetic and optical mass storage devices and/or internalmemory, such as RAM chips. The memory and/or storage components may beused for storing programs and routines for performing the techniquesdescribed herein that are executed by the processor 304 or by associatedcomponents of the data management system 300. Alternatively, theprograms and routines may be stored on a computer accessible storagemedium and/or memory remote from the data management system 300 butaccessible by network and/or communication interfaces present on thecomputer.

The data management system 300 may also include various input/output(I/O) interfaces, as well as various network or communicationinterfaces. The various I/O interfaces may allow communication with userinterface devices, such as a display, keyboard, mouse, and printer thatmay be used for viewing and inputting configuration information and/orfor operating the system 300. The various network and communicationinterfaces may allow connection to both local and wide area intranetsand storage networks as well as the Internet. The various I/O andcommunication interfaces may utilize wires, lines, or suitable wirelessinterfaces, as appropriate or desired.

In one embodiment, the device 306 is turned on as for continuous viewing(with a notification) by the processor 304 when images are indicative ofa significant potential failure and/or cascade process or at a pointwherein the patient's risk class exceeds a threshold value. The riskclass may, for example, be derived as a function of a calculatedinstability index or a detected instability index pattern and/ordetected failures. The instability index may be, for example, aconfidence metric correlated with a matched image. For example, when anMPPC has a high likelihood of being associated with a serious condition,the instability index may be high. The instability index may be anumeric index, a color or graphic indicator, and/or an audio or textmessage.

In accordance with an embodiment, the device 306 includes an interactivescreen displaying items, such as one or more working diagnoses,differential diagnosis, parameters derived from patients includinglaboratory parameters, monitored parameters, and subjective parameters(e.g., sedation scale, confusion scale, or pain scale) or the like. Inan embodiment, the term “parameter” herein may refer to an absolute orrelative data point or set, a pattern, or a deviation, a range of suchdata points or sets, a pattern of such data, a relationship along asingle set of data and/or or between a plurality of sets of data, and/orpatterns of data. The data may be an objective data type or subjectivedata type and may be directly and/or indirectly derived or historical inorigin. In addition various outputs from the failure imaging processor360 (FIG. 3B) may be displayed. According to on embodiment, theprocessor 304 may provide data for display present on the device 306 orthrough a report (either electronic or paper) or within an electronicrepresentation that may provide an interface to external systems.

The data management system 300 further includes a medical recordsdatabase 308 including laboratory data 310, historical data (e.g.,diagnosis) 312 and therapy data (e.g., medications) 314. The medicalrecords database 308 is coupled to the processor 304 and to the monitor302 so that those systems may have access the data stored in the medicalrecords database 308. The processor 304 may include a component ordirect link to the centralized patient medical record, which containsreal time data and receives data input from all hospital sources. Thus,a database containing substantially all of the components relating tothe patient available to the hospital may be directly accessible to theprocessor 304 in real time to allow the embedded relational processorrender relational binaries, and construct and detect failure imagecomponents which include these data from varied sources.

In accordance with an embodiment, the processor 304 is adapted tocomprehensively engage the medical records database 308. As discussedfurther below, the processor 304 may be programmed to provide forformal, automatic simultaneous engagement, of physiologic failure imagecomponents, medication failure image components, testing failure imagecomponents, aggregate failure image components as derived from therelational processor and to render them in a timeline for viewing.

The processor 304 may be adapted to provide an immediate review of allfailure image components and to take action based on the detection ofspecific failure image components. The processor 304 may be capable ofresponding faster and more reliably than the healthcare worker becauseit may be adapted to constantly monitor the evolving failure imagecomponents form the earliest onset of the first divergent binary. Theprocessor 304 may therefore detect failure image component cascades,which originate from single divergent binaries, which might easily beundetected by the healthcare worker until it is too late. The processor304 may also be programmed to alarm on divergent or null binaries uponwhich no action has been taken or upon which the action has notcorrected the evolving divergent binary or failure image component. Forexample, in a scenario in which the processor 304 has been updated bythe nurse that a blood culture has been obtained, the presence of a nullbinary may be generated indicating testing failure image component ifafter a pre-selected time the result is not available to the processor304 whereas the presence of a divergent binary indicative of aphysiologic failure image component may be detected if the culture ispositive. If testing failure image component is detected the processor304 notifies the lab of the apparent delay. The notification is an alphaevent and a receipt response to that notification is a true beta event.Therefore the failure of the lab to indicate receipt may cause theoccurrence of a divergent binary, which may trigger the notification ofthe nurse in the same manner until a convergent binary concludes thesequence. If on the other hand, a physiologic failure image component isdetected (the culture is positive), the processor 304 notifies the nurseagain in the same binary generating fashion.

While a positive blood culture is the beta event of the culture testingbinary, it is the alpha event for another group of testing binaries suchthat the initial divergent testing binary may cause the processor toassure acquisition of a complete blood count, a comprehensive metabolicprofile, increased frequency of blood pressure and pulse measurements,ventilation indexing oximetry and other testing as programmed into theprocessor 304 in response to the specific divergent binary detected (inthis case a positive blood culture). These new testing binaries maygenerate unexpected beta events (such as a low blood pressure, a highpulse, or high ventilation to oximetry index) and these beta events maythereby define a new set of divergent physiologic binaries. This new setof divergent binaries (in aggregation) may be sufficient to meet thepre-selected criteria of an aggregate failure image component suggestiveof early septic shock, which diagnostic consideration now includes analpha event to a plurality of new binaries which have been programmedinto the processor to assure timely and proper monitoring, timely properpatient location, and timely proper diagnostic testing, and timely andproper intervention in the event of the detection of this type ofaggregate failure image component. In addition, the beta events of thedivergent physiologic binaries which included the aggregate failureimage component now become alpha events for new physiologic binarieswherein the beta event of each of the new binaries includes the returnof each these values back to a normal range within a pre-selected timeperiod (thereby assuring, that the aggregate failure image component iscorrected timely, if possible). In additional, the positive bloodculture is also the alpha event for a treatment binary such that theprocessor 304 may be expecting to see the correct antibiotic in responseto positive blood culture administered within a pre-selected timeinterval. If this does not occur a divergent binary indicating treatmentfailure may be identified and assured nurse notification may proceed bythe binary building method previously discussed.

According to one embodiment, in response to the detection of anysignificant divergent physiologic binary, the device 306 may beprogrammed to prevent the failure of notification by building a setnotification binaries, which must end with convergence. The device 306may also be programmed to prevent failure to timely treat by building aset of treatment binaries, which must end with convergence. Further, thedevice 306 may be programmed to prevent failure test by building a setof testing binaries, which must end with convergence. The device 306 mayalso be programmed to detect associated physiologic failure imagecomponents by identifying divergent physiologic binaries in associatedwith the initially discovered divergent binaries.

According to one embodiment, the processor 304 includes an associated,connected and/or embedded eventing system. In this eventing subsystem,users may designate actions to be initiated or data to be recorded whena specific occurrence is identified. This eventing system may interfacewith other internal or external systems including notification systems,workflow systems, asynchronous communication systems, reporting systems,decision support systems, dashboards, data warehousing and/or datamining systems to name a few.

According to one embodiment the relational processor is self-modulatingand provides an automatically expanding analysis, which is rapidlyresponsive to the occurrence of even a minor failure image component.The analytic activity of the processing system is capable ofmultidimensional growth and diminishment in direct response to themagnitude and number of failure image components detected. In thisregard, the processor 304 upon the occurrence of a physiologic failureimage component may generate a cascade of notification, testing,treatment, and physiologic binaries even if that failure image componentincludes only a single physiologic divergent binary. The beta event ofthe physiologic binary may include the alpha event of each of a newgeneration of notification, testing, treatment, and physiologicbinaries. Each of these new binaries also have a beta event, each whichmay induce the formation of other binaries wherein the beta eventincludes the alpha of another binary of the same or another type. Aspontaneously growing cascade of binaries thereby evolves towardassuring timely notification, timely testing, and timely restoration ofphysiologic stability.

A rapidly expanding, cascade of these types of divergent binariesindicates evolving patient instability of the patient or poorperformance of the healthcare system. An analysis (as by objectifiedpattern recognition or statistical analysis) of the timed patterns ofthe types and sequence of the divergent binaries may allow thedetermination of poor health or poor responsiveness of the healthcareworker is causing the cascade to be propagated. As health is restored,and provided the healthcare workers are timely responsive, the binarycascade may automatically diminish and the various failure imagecomponents may no longer be detected. The outputs of the relationalbinary object processor therefore provides a self modulating processingsystem which may be readily used and further analyzed to track thehealth of a single patient, or the patients on a given floor, or thepatients hospital wide. The outputs of the object binary processor alsoprovides a self modulating processing system indicative of the qualityof healthcare delivery provided to a given patient, on a given floor, orhospital wide.

The processor 304 may be applied to other complex dynamic data setsother than medical data wherein a self-modulating relational analysisand control would be useful. The processor 304 has utility for the datamining, for example in association with the processing of archiveddatasets to identify the failure image component process from theinitial spark (the first divergent binary) to extensive system failure.The processing of archived datasets provides the opportunity to reviewthe automatic modulation of the binary cascades which are derived ofvarious failures and to facilitate the construction of dynamic failureimage component diagrams for complex processes in the hospital, as wellas in industrial processing such as the food, chemical, orpharmaceutical processing. The processor may be programmed such that theuser may select each alpha event and allow the processor to detect,offer, and/or derive events and relational binaries, which havespecified temporal, frequency, or spatial relationships with theselected event object. Alternatively the processor 304 may be programmedto construct its own set of convergent object binaries with a learningdataset by processing the outputs of healthy individuals and then theprocessor may be used to detect divergent binaries when applied topatients by identifying the lack of the expected beta events (which weredefined by the learning dataset). Sensitivity for cascading (theinitiation of further processing based on the detection of a divergenceor a failure image component) may be adjusted by modifying thesensitivity for trueness of the beta event or by modifying the criteriasuch as slope, or magnitude of the objects during the objectificationprocess. This provides a high degree of flexibility in definingsensitivity to the designation of a binary as divergent and thistherefore allows a high degree of control over the sensitivity tocascade initiation, propagation, and extinguishment. Cascades may bemodular or divergent or failure image component specific. A modulargroup of cascades may be selectable from a menu and then each one in thegroup modified as desired.

As shown in FIG. 3B, the processor 304 may include instructions for anynumber of processing functions. As shown the processor 304 may includean event editor 331 (creates event definitions 332), a convergenceeditor 343 (creates binary definitions sets 344), and a failure imagecomponent 355 (creates failure components 356). The event definitions332, binary definitions 344, and failure components 356, may be used aninputs for the time series objectification processor 336, the relationalbinary processor 348, and the failure imaging processor 360. The timeseries objectification Processor 336 is programmed, with the rules andparameters provided by the event definition set 332, to convert paralleltime series (324, 328) of the electronic medical record 320. Therelational binary processor 348 then, with the rules and parametersprovided by the binary definition set, processes the object stream s 340to generate stream a and cascades of relational binaries 352. Furtherthen, the failure imaging processor 360, with the rules and parametersprovided by the Failure image component definition set 356, synthesizesthe relational binaries, and in some cases isolated objects from theobject stream, into one or more images 364. The output of each of thesethree processors (336, 348 and 360) as well as the original time seriesupon which they were applied is stored in an MPPC database 368. In anexample, the processor 304 may be programmed so that detection of one ormore events, binaries, image components or detection of a specific MPPC,may cause the processor to take action such as provide an outboundnotification of the detection, orders for testing or treatment, ordirect control signals to a treatment and/or testing device to change,cease or initiate testing and/or treatment.

According to one embodiment, the relational binary processor 348 and thetime series objectification processor 336 may adapt to the output ofeach other to modify the analysis. For example, the detection of anevent, a reciprocation, an incomplete reciprocation or other objects orpatterns by the time series objectification processor 336 may cause anadjustment to the cascade responsive to the detection of a divergence.Alternatively or in combination the criteria for designation of a wavesegment as an event object within the time series objectificationprocessor 336 (for example the slope criteria for identifying a fallevent object of serum sodium) may also be adjusted based on the presenceof a specific alpha event. In an example, when an alpha event includinga diagnosis of cerebral vascular infarction (CVA) is detected, this maycause the time series objectification processor 336 to reduce theabsolute slope (less negative slope) for designating a fall event objectof serum sodium, which, is preferably one of the betas in such patients.By automatically reducing the absolute slope for the designation of thebeta event the alpha diagnosis of cerebral vascular infarction isadjusting the sensitivity of the diagnostic process allowing automaticand dynamic adjustment upon the occurrence and detection of differentphysiologic vulnerabilities. In this example, the increase insensitivity for detection of a fall event object in serum sodium (which,combined with the alpha that includes a CVA diagnosis) would include adivergent binary), which may trigger a diagnostic cascade for closemonitoring of the serum sodium and/or the evaluation of additionallaboratory studies and/or the reduction of free water delivery. This isdesirable due to the unique vulnerability faced by patients with CVA asa function of the potential for inappropriate increase in anti-diuretichormone due to the CVA.

Since the relational binary definitions within the binary definition set344 may be individually defined and refined by processing largepopulations of historical data, correlations may be verified, ratherthan being simply proposed and maintained as a function of consensus orexpert opinion. In one embodiment, cascades originated by criteria fordivergence provided by an expert, which untimely lead to extinguishmentwithout intervention may be automatically adapted to either change thesensitivity for the detection of the divergent beta or to change thecascade resulting for the divergent binary. In another example, cascadesoriginated by criteria provided by an expert which continue selfpropagate and expand despite timely action and without progression ofthe physiologic divergence may be automatically adapted to either changethe sensitivity for the detection of the divergent beta or to change thecascade resulting for the divergent binary. The sensitively andspecificity may be further enhanced because the system may be applied toarchived training data sets wherein the outcomes are known so themagnitude and direction of the cascades may be compared to the desiredmagnitude and direction of the cascades and adjusted accordingly. Withapplied archived datasets the application of auto-adaptive adjustment inevent criteria, divergence criteria, or cascade generation may beapplied until the cascades proceed without premature auto extinguishmentand excessive propagation. Furthermore the system may be applied tohypotheticals on the missing data to allow determination as to how theymight affect incomplete (null) binaries.

According to one embodiment the processors, including the time seriesobjectification processor 336, the relational binary processor 348 andfailure imaging processor 360, may output the results of their analysisinto the MPPC Database 368. The MPPC Database 368 contains the timeseries 328 on which the analysis was performnned as well as the resultsof analysis including the event stream s 340, the relational pairs 352,the aggregate failures 364 as well as aggregations, relationships andalternative images of these elements. In one embodiment, the metadatarule-sets (both primary and alternative and/or temporarily overridden oraltered elements) are persisted as XML (event definition set 332, binarydefinition set 344, Failure image component definition set 356) in thepatient safety image database 368.

According to one embodiment, a processor is programmed to rendersequential time series components (which may be discrete and/orsuccinct) and which may be subsequently linked other sequential timeseries components along and across parallel time series to produce acomprehensive relational image of physiologic failure and/or patientcare. These components may be rendered by methods defining polarityreversal or inflection points, state changes, by imaging methods todetect pattern components (for example subsequent to time seriesrendering into a particular format for example), and/or by anothermethod for defining and/or programmatically “packaging” events, imagecomponents, and/or occurrences for relational imaging and analysis.According to one embodiment “time series objectification” is employedfor this purpose. Time series objectification may be rendered by a timeseries objectification processor 336, an embodiment of which isdiscussed below. In one embodiment, time series objectification is theprocess of converting a set of time series into a stream of sequentialdiscreet elements or objects such that substantially the entire timeseries of data is converted to a time series of objects in a relationalhierarchy of ascending complexity. In another embodiment these objectsare created by identifying boundaries within the time series based onthe values of the points within the time series (for example thresholdviolation and/or state match to name a few) and/or the relationshipbetween these points (for example polarity reversal, inflection point,state transition to name a few) using a set of rules based on anunderstanding of phenomena within the system from which the time seriesare derived or which are learned adaptively. The discrete objects whichare created represent and characterize an occurrence providing a timelocation and a set of properties derived from the aggregated data withinthe boundary defined. These objects are differentiated by location andthe properties derived and therefore individual objects can be qualifiedand the stream of objects can be searched against. Further, theconversion to discrete objects provides for the identification,qualification and searchability of relationships between elements.Relationships can be converted into aggregations and/or hierarchy ofelements within which properties can be derived from components of anaggregation/hierarchy to the aggregation/hierarchy itself and/or fromthe hierarchy/aggregation to the participating components.

A time series objectification processor 336 may for example, containinstructions as provided in U.S. patent application Ser. Nos.11/280,559, and 11/351,449 the specifications of which are incorporatedby reference herein in their entirety for all purposes. Accordingly,such processors may function by constructing a time series matrixincluding of substantially all of the parameters derived during theprocess of the hospitalization and then objectify each time series inthe matrix to produce an objectified time series matrix. The time serieswhich include the matrix may, for example, include objective measuredvalues, drug dosing, infusion rates, and subjective clinical scores toname a few. At least some of the time series may be provided as a stepfunction. For example, time series of the weights, serum sodium values,SPO₂, ventilation volume or rate, heart rate, pulse amplitude, pulseslope, drug infusion dose, sedation score, pain score, stupor score,working diagnoses, an instability score, a severity of illness score, toname a few, may all be included.

The objectification processor can define objects by a wide range ofmethods which may be programmatic and/or image based or by anothermethod of defining objects. In an embodiment, a time seriesobjectification processor applies a linear and/or iterative dipole slopeapproach to the recognition of waveform events, as for examplerespiratory or oxygen saturation events. For example, the eventsassociated with airway collapse and recovery are generally precipitousand unipolar, for this reason the linear method suffices for therecognition and characterization of these nonlinear waves. However, theiterative dipole slope approach is particularly versatile and may beused in situations whereby the user would like an option to select theautomatically identification of a specific range of nonlinear or morecomplex waves. Using the iterative dipole slope method, the user mayselect specific consecutive sets of points from reference cases along awaveform as by sliding the pointer over a specific waveform region.Alternatively, the user may draw the desired target waveform on a scaledgrid. The user may also input or draw range limits thereby specifying anobject or set of objects for the microprocessor to recognize along theremainder of the waveform or along other waveforms. Alternatively, theprocessor may automatically select a set of objects based onpre-selected criteria that may be empirically determined. Since theiterative dipole process output may be shape-dependent (includingfrequency and amplitude) but is not necessarily point dependent, it ishighly suited to function as a versatile and discretionary engine forperforming waveform pattern searches. In accordance with embodiments,the waveform may be searched by selecting and applying objects tofunction as Boolean operators to search a waveform. The user may specifywhether these objects should be in the same order. Recognized objectsequences along the waveform may be scored to choose the degree of matchwith the selected range. If desired. (as for research analysis ofwaveform behavior) anomalies within objects or occurring in one or moreof a plurality of simultaneously processed tracings may be identifiedand stored for analysis.

After the process of objectification and further processing of the timeseries matrix (e.g., generated from object stream s 340) the images aretransferred to the patient safety visualization processor 372 whichpresents and highlights the detected MPPC on an outputted display orpatient safety console 306 or through a patient safety report 380(either electronic or paper) or within an electronic representation asan interface 376 (for example the European Data Format (EDF)) which mayprovide an interface to external systems. In one embodiment, theaggregation of data, analysis and metadata provide the source of datafor the patient safety visualization processor 372. In one embodiment,the Patient Safety Visualization Process 372 provides a visualization ofa patient's condition in a comprehensive grouping defined by rows oftimelines of specific signals and/or grouping and/or categories ofsignals and/or signals. In one embodiment the global state of each rowis represented by color in a spectrum with a different color moving fromstability to failure (for example. Sustained Stability [deep blue],Stability [light blue], convergence [green]. Perturbation [yellow],Divergence [orange], Null [black], Failure [red]. Cascading Failure[bright red]). In another embodiment colored arrows, icons, blinking orhighlighted text, and/or other visual representations along each timeline represent these states.

In one embodiment the patient safety visualization processor 372represents the patient condition as a set of pixel stream s moving fromleft to right to show evolution of condition over time. The processorprovides the navigation backward and forward in time as well as up anddown through levels of analysis within the patient safety image database368. In this embodiment the levels of analysis may be, for example:

time series—Unanalyzed data stream s in the form of time series

events and Perturbation—events, state matches, state transitions andthreshold violations characterized within their respective channels asto whether they represent clearly defined perturbation according to theevent definition set 332

Systemic Response—convergent, divergent and null binaries representingthe relationships between events, state matches, state transitions,threshold violations, perturbations and expected elements according tothe binary definition set 344

Failure−Failure images that have been identified within a single patient

System Failure—Failure images within a specific category (such as therespiratory system) representing images of failure that have beenidentified within a single patient

Failure Patterns—Trends of failure and failure images within patientpopulation or a specific region, such as a specific hospital ward forexample.

In one embodiment the patient safety visualization processor 372composes an image on computer monitor (the patient safety console 306),which may be composed by a series of pixels oriented horizontallyrepresenting data and analysis stream s. These pixel stream s may bestacked vertically with the position on the x-axis representing aspecific point in time. The processor provides for the movement of thepixel stream s horizontally to provide a pan through time.

Each pixel stream may be composed of a set of pixels, which indicate thestate of the data and/or analysis at the specified point in time. Thepixel has a state (e.g. represented by color) and granularity (thelength of time it represents [for example 1 minute]). The size of theview as well as the selected span of time determines the granularity ofthe pixel. In the contemplated embodiment, the pixel is displayed by thehighest level of instability found within the time span represented bythe single pixel within the pixel stream. Further, each pixel has alevel of abstraction, which determines which objects from the patientsafety image database 368 contribute to its state. The contributingobjects are shown below by level of analysis:

time series—Data points within the channel (e.g. oxygen SaturationValues)

events and Perturbation—events and threshold violations

Systemic Response—Relational binaries

Failure−Failure images

Failure Patterns—Failure trends and correlations.

In one embodiment, groups of pixel stream s are stacked vertically tocreate a patient safety visualization. Patient safety visualizations maybe composed of pixel stream s of different patients or of data andanalysis stream s within a single patient. Patient safety images providethe ability of the care worker to filter the analysis quickly toidentify problem areas or areas of a specific nature. Sorting may beprovided highlight emerging failure cascades or other pattern failures.In an embodiment patient safety images may be composed of differentlevels of analysis displayed on the patient safety console 384 at thesame time correlated by time. The use of mixed-analysis levelvisualizations provides the care worker with the ability to quicklyunderstand the relationship between the lower levels of data (e.g.incomplete recovery within oximetry) and the higher levels of analysis(e.g. the identification of narcotic-induced ventilation instability).

In an embodiment the patient safety console 384 provides the user theability to trace a failure condition back to the earliest eventsassociated with the failure to provide a visual display of a failurecascade. Alternatively, individual events and threshold violations maybe selected to identify which higher-level objects in which they playeda part. In other words, low-level events may be traced forward tounderstand their relationship within evolving patient instability. Thistracing may be accomplished in many ways. For example, the processor 304may exploit the fact that Alpha events of a relational binary are oftenthe Beta event of a preceding relational binary. This chain ofrelational binaries provides a powerful tool of analysis. The patientsafety visualization processor provides the ability to isolate thesebinary Chains showing their origin, evolution and resolution.

Alternatively, and in concert, the processor 304 may use the trend ofprobabilistic momentum. In one embodiment, visualizations may befiltered by the existence and character of binary Chains or arecognizable trend of Probabilistic Momentum. In one embodiment, and ifselected by configuration, the patient safety visualization processorprovides the ability to navigate into the metadata models at any pointwithin the visualization, event, convergence and image Diagrams or otheroccurrence definition Visualizations are accessible from objects, whichwere composed using specified elements within these diagrams within theevent definition set 332, binary definition set 344 and image definitionset 356. Navigation into the metadata models provides expert careworkers and researchers the ability to further understand and/or alterthe analysis.

The patient safety console 384 presents a complex set of data andanalysis that meets the immediate need of the busy care worker. In oneembodiment, analysis at the highest levels may be collapsed into asingle pixel stream or group of pixel stream s per patient that providesa simple representation of the evolution of overall patient safety.Within and from that pixel stream the care worker may drill down intothe most complex displays: multiple levels of analysis, binary Chains,trends of Probabilistic Momentum and metadata models to name a few.Alternatively this drill down may be provided by for example mouse over,touch screen, or may appear automatically when the processor detectscertain adverse patterns or thresholds.

In one embodiment, the object stream visualization focuses on therelationships and cascading of the onset of perturbation within thepatient. This is an alternate, and complimentary, view to the pixelstream s described above which focus to a greater extent on the state ofdiscrete elements within the system at various levels of analysis. Thesetwo visualizations may be used in parallel and/or provide navigationbetween them. In the contemplated embodiment, the object streamvisualization represents events and threshold violations as icons alonga time series in which the icon is placed at the first point in time inwhich the event or threshold violation occurred. Icons indicate theircharacter by color, size and decorations. The basic icon is an arrowpointing either up or down (as in FIG. 15A). An up arrow indicates apositive movement, which triggered an event whereas the down arrowindicates a negative movement. Boolean changes will be indicated as anup arrow when moving from false to true and a down arrow when movingfrom true to false, state matches are indicated as an up arrow duringmatch, state transitions are indicated as an up arrow when moving intothe state indicated and a down arrow when moving out of the stateindicated, threshold violations are indicated as an up arrow if thethreshold violation is defined in terms of “greater than” and a downarrow if the threshold violation is defined in terms of “less than”. Thethickness and/or color of the arrow may be used to indicate the extentof movement.

Decorations on the arrow may be presented to provide visual cues as tothe nature of the event. A line underneath the head of the arrowindicates that he event that occurred was a threshold violation. Acircle around the arrow (see 979 of FIG. 15A) may be used to indicatethat the event was the result of a action or test ordered by theprocessor 304. Decorations and/or matching colors and/or flashings maybe used to indicate a relationship warning by the processor, as in thewarning of the potential relationship between the low platelet count andthe medication clopidogrel in FIG. 18.

In one embodiment, the patient safety visualization processor willprovide automated visual navigation for a specified period of timeand/or specified images. This automated visual navigation acts as ananalysis-driven video playback of the selected period of time. Thehealthcare worker selects “Play” and allows the patient safetyvisualization processor to move visually through the evolution of aspecified condition. The healthcare worker may choose navigationmovements including “Play”. “Pause”, “Fast-Forward”, “Rewind”, “SkipForward”, “Skip Backward”, to name a few. In the contemplatedembodiment, during Play mode the patient safety visualization processormoves at different speeds through the automated visualization dependingon the severity of the condition being displayed. If the time seriesbeing displayed have little perturbation (or little perturbation relatedto the specified failure cascade) the processor will move very quicklythrough time (i.e. from left to right). When an area of interest, asdetermined by the processor, comes into vision the patient safetyvisualization processor will slow the movement from left to right.Further, the patient safety visualization processor will highlightelements that indicate, clarify and specify the evolution and/or cascadeof failure as well as their relationships with other elements. Thepatient safety visualization processor will further display translucentpop-up panels that provide further textual and/or visualization elementsto describe the current view and elements within the current view. Atany point, the healthcare worker may “Pause” the automated visualnavigation to review the displayed data and/or drill into what has beendisplayed.

In a complimentary embodiment, the healthcare worker may select from asummary view a time span to review and also indicate sections of thetime span for which they are interested. The patient safetyvisualization processor will slow for the areas selected that are ofinterest and will increase the textual and visualization displayappropriately for the highlighted sections. In one embodiment thepatient safety visualization processor chooses the object stream s todisplay and may include or remove stream s as they become important inthe video navigation. The healthcare worker may choose to includeadditional stream s or to “pin” stream s so as to make them alwaysavailable in the video navigation, missing stream s are also indicated.The patient safety visualization processor may further indicate to thehealthcare worker the time required for automated visual navigation(e.g. “Standard visual navigation will require 2 minutes and 37seconds”).

The patient safety visualization processor will include audio and visualelements corresponding to and synchronized with the time series dataalong with time series data if video and audio feeds are available. Inthe contemplated embodiment, healthcare workers may include audio and/orvideo comments into the data stream s to communicate and collaborateregarding elements displayed within the patient safety visualizationprocessor. The patient safety visualization processor may be directed toinclude all or a specified subset (e.g. “Include Comments from DoctorX”) of these elements within the automated visual navigation or may bedirected simply to indicate their presence such that the healthcareworker may invoke them as needed.

In one embodiment, the patient safety visualization processor may“record” an automated visual navigation session into a non-interactivevideo format which may be viewed on standard video equipment, withstreaming technology or in a standard media player such that automatedvisual navigation sessions may be shared with healthcare workers who donot have access to the patient safety image database or the patientsafety visualization processor (e.g. as an attachment to an e-mail oraccessed from a video-enabled phone).

FIG. 4 depicts a UML static diagram of the time series matrix within oneembodiment of the processor 304. According to the depicted embodimentthe most basic time-related element within the processor 304 is a point412. A point 412 is the value of a signal at a particular instance intime. A point does not have duration, but is locatable in time(implements the interface time locatable 406). Points may be aggregatedinto an ordered collection within a point stream 410 which is a type oftime series 404. Point streams 410 are often referred to as signals.Point streams 410 have two modes 408: numeric and non-numeric. Numericstreams include points values that are integer, floating point, doubleprecision, decimal, positive integers to name a few. Non-numeric pointstream modes include point values that are Boolean, domain and freeformstring to name a few. Point streams 410 may be continuous ornon-continuous. Continuous point stream s have a single sample rate andthe points contained therein are equidistant apart in time.Non-continuous point stream s may have any distance between the pointsin the stream and include a time with each point in the stream .pointstreams 410 represent one kind of time series 404, occurrence stream s414 represent another. An occurrence 418 is an object representing ahappening within time. An occurrence 418 (as show in FIG. 5) may be anevent, a relational binary, an image, a repeating occurrence or apattern occurrence to name a few. An occurrence is locatable in time(implements the interface time locatable 406) but also spans time andtherefore has a start time and end time (implemented with a time bound420 interface), occurrences 418 may be aggregated into a stream orderedby time called an occurrence stream 414. Occurrence stream s may alwaysbe converted into a Boolean point stream 410 by creating points with thevalue “True” for all points in the span in which an occurrence existsand “False” for all points in which an occurrence does not exist.

Having a flexible time series 404 supports the construction of the timeseries objectification matrix. For example raw signals from monitoringequipment may be contained within numeric point stream 410 whereascomplex micro-domains such as images may be stored in occurrence streams 414. Both of these stream s may be treated similarly as a time series404. The time series matrix 400 is simply an aggregation of supplied andderived time series 404. The fact that all elements within the streamsare time locatable gives the matrix the parallelism that allows timerelationships to be identified and aggregated into the scope of anoccurrence 418.

Each time series 404 has a single type (time series type 402). Forsignals this may be defined as the signal type (e.g. oximetry, EKG). Thematrix 400 may contain many time series of the same type. For occurrencestream s the time series type is defined by the occurrence definitionassociated with the definition (e.g. oximetry fall event, NarcoticInduced Instability image). A time series may be broken up intosub-spans called time series segments 416. Both the time series 404itself and all associated time series segments 416 are time bound 420(e.g. have a start and end time).

FIG. 5 is a UML static diagram that depicts, according to oneembodiment, a model of the occurrences that may be resultant from amatrix Construction process. Recall from FIG. 4 that the time seriesmatrix 400 is constructed of two types of time series 404—point streams410 and occurrence stream s 414. FIG. 5 focuses on the fundamentalelement of the latter of these two—the occurrence 448. Note that theoccurrence on FIG. 4 418 and the occurrence on FIG. 5 448 are two viewsof the same class. The occurrence class 448 is an abstract class(meaning that it is a conceptual class from which other classes arederived and which when instantiated must be sub-classed). The occurrence448 object represents a happening in time. FIG. 5 depicts severalsubclasses of occurrence 448 which the processor 304 may identify andcreate during the matrix creation process. The simplest of thesesubclasses is the event 474. An event 474 is an occurrence within asingle point stream, events 474 are further subclassed into 4 types:directional event 480, threshold violation 484, state match event 482and state transition event 478, directional events 480 represent anidentified unipolar pattern within a point stream 408, thresholdviolations 484 represent the existence of a breach of some specified,calculated or derived limit within an associated point stream, statematch events 482 represent time in which the value within the pointstream matched at least one element in a set or fell within a domaindefined by a set definition mechanism. A state transition event 478represents the change from one state to another as defined by pointsmatching an initial set and subsequent points matching a second seteither by matching a value or by falling within a domain defined by aset definition mechanism. Alternatively, state transition events 478 maybe defined by specifying a state flow diagram or state machinedefinition and the beginning and end state for the transition.

The processor 304 defines an isolated event as an event identifiedindependent of a relational binary, in other words, an event that doesnot belong to any relational binary. In an example of a embodiment, arange of events may be defined within a single definition. Range-baseddefinitions allow the user to define all but one parameter within theevent definition. For that final parameter, rather than a single valueor expression, the user is allowed to define a set of value ranges inwhich the final parameter may fall. For each range provided, the type ofevent is specified. In this way a set of related events may be defined.

The processor 304 employs a multistage approach to the identification,creation and refinement of event objects. An initial set of criteria isused to establish the time location of a time series segment, which isestablished as an event candidate with a specific start and end timewithin a single point stream. Once this time series segment has beenestablished it becomes a micro-domain within which further propertiesand elements may be derived. For example, within events established on anumeric time series that are specific to a unipolar trend of data (e.g.directional events 480), the processor 304 will further refine the trendby looking for smaller changes in the trend. The processor 304 callspoints of change within the trend Inflection points 486. Once inflectionpoints are determined then the event is broken up into smaller segmentscalled event segments 488. In one embodiment Inflection points 486 arederived in three ways:

-   -   1. At each point within the candidate event, the slope        difference between the preceding and subsequent dipole is        measured. If the absolute value of this difference meets a        specific threshold then the point examined is designated as an        Inflection point 486.    -   2. Moving through the candidate event, the processor 304        establishes an initial slope as the slope of the first dipole        within the candidate event. The processor 304 then creates a        series of evaluation event segments using an increasing number        of dipoles (first two, then three and so on) within the        candidate event. For each of these evaluation segments the slope        is calculated. If the absolute value of the difference between        the initial slope and the evaluation segment slope meets a        specified threshold then the processor 304 determines that an        inflection point has occurred within the evaluation segment.        Then the evaluation segment is examined at each point (starting        with the second point) calculating the absolute value of the        difference between the slope of the segment before the point and        the slope of the segment after the point. When this difference        is greater than 50% of the threshold used to establish the        existence of an Inflection point 486 then the point being        examined is determined as the Inflection point 486 and a segment        is created from the beginning of the candidate event to this        point. Once this inflection point has been established then the        first dipole after that point becomes the initial slope to be        evaluated against and the process begins again until the end of        the candidate event is reached at which, if any inflection        points were found, the final segment is added as an event        segment.    -   3. Finally the slope of each dipole may be placed into a time        series and that time series examined looking for state        transition events 478. Each point at which a state transition        occurs is established as an inflection point and the intervening        dipoles are aggregated as event segments. For example, within        oximetry the researcher may establish two slope ranges—slow        descent and rapid descent. The processor 304 will walk the        dipole stream looking for a point at which the previous and        subsequent dipoles fall into the two different ranges. When this        occurs, the processor 304 would designate the point as an        inflection point (designated as “Change from slow descent to        rapid descent”) and the event segments created would be        characterized “Slow Descent” and “Rapid Descent”.

The creation of sub-objects (e.g. inflection points and event segments)within an event provides a wide array of elements against which thedefinition of the event may be refined. For example, the researcher maydecide to create an event type 476, which must include a “Slow Descent”segment followed by a “Rapid Descent” segment. The researcher may chooseto remove the “Slow Descent” portion of the candidate event from thefinal event, or split the candidate event into two separate events.Alternatively, the researcher may not choose to use these elements andtheir properties as part of the criteria (e.g. used to accept or reject)but may use them to characterize the event (as per, for example, theassigning of attributes described below).

For each event 474 there is an event type 476 which identities the typeof micro-domain which has been created. The event type is associatedwith the definition used to identify, construct and qualify the event.For example, a researcher may create an oximetry Rise event as an eventtype 476 which defines a positive directional event 480 within theoximetry point stream. The definition for the event (depicted in FIG. 8a) provides the parameters which the processor 304 uses to search for,construct and qualify an oximetry Rise event. The event type 476provides the researcher a way to specify a particular pattern which thenmay be associated with other more complex occurrences (e.g. an image).In this way the event type 476 provides an abstraction which simplifiesthe reference to a particular pattern. The term type is used this waythroughout the UML diagrams as the reference to a particular definition.The type, therefore, provides the link between the instance of a pattern(or other object) and the definition of a pattern (or other object).

Another subclass of occurrence 448, within this embodiment, is therelational binary 462. The relational binary comes in two subclasses:occurrence binary 466 and event binary 468. The relational binary isdepicted more completely on FIG. 6. The binary type 464 expresses thespecific binary pattern found as defined in the uniquely associatedbinary definition (See FIG. 88). Another subclass of occurrence 448,within this embodiment, is the image 452. The image 452 represents anaggregation of one or more occurrences 448. The image type 454 expressesthe specific occurrence pattern found as defined in the uniquelyassociated image definition (See FIG. 8C).

During matrix construction, the processor 304, by default, aggregatesand analyzes repeating objects. The processor 304, after identifying anoccurrence, reviews the object stream for other occurrences proximatewithin the associated stream s. By default, the processor 304 looks forrepeating occurrences 432 (e.g. occurrences of the same occurrence mode(e.g. relational binary) and occurrence type (e.g. oxygenreciprocation)). Each occurrence type definition specifies a minimumrecurrence count as well as a recurrence threshold time span by whichthe processor 304 may determine whether or not to aggregate occurrencesof the same type into a repeating occurrence object. In one embodiment,the recurrence threshold may be specified either as a fixed time span oras a series of time spans depending on the state of the aggregationprocess. For example, the definition may include an initial recurrencethreshold (a maximum time span allowed between the first and secondoccurrence) and a subsequent recurrence threshold (a maximum time spanallowed between any subsequent two occurrences). In one embodiment, thisthreshold may be a function (e.g. the time being increased incrementallyby the number of occurrences that have already been aggregated).

Once the processor 304 has identified a repeating occurrence 432 and hasaggregated occurrences within this object, the processor 304 will createa point stream 442 for all properties of the specific type of occurrencebeing aggregated called a property channel 442. FIG. 27 provides adiagram to illustrate the role of property channels 442 within arepeating occurrence 432. For example, consider when the processor 304is searching for a specific occurrence 1280 which is an oxygen fallevent. If the processor 304 has aggregated 9 fall events into arepeating occurrence 1284 then a property channel 1286 (442 in FIG. 5)will be created of the slope of the fall events within the aggregation.In FIG. 27, the circles (1281, 1282, and 1283) attached to theoccurrence 1280 represent properties of the occurrence. In this case,the property channel 1286 (442 in FIG. 5) will have 10 points (shown asA_(z) through I_(z)) with each point representing the magnitude of onefall event within the repeating occurrence 1284 (432 in FIG. 5). Inanother example, the nadirs of the oxygen reciprocation would constitutea property channel 442.

These property channels 442 are attached to the repeating occurrence 432and may be accessed through the repeating occurrence 432 object.Further, these property channels may be analyzed through the time seriesobjectification processor, the relational binary processor and theImaging Processor to create occurrences within and between the propertychannels and/or between occurrences within the property channels andother point streams or occurrence streams in the system. For example, inFIG. 27 the points in the property channel 1286 (which represent themagnitude of fall events (the property designated as ‘Z’ 1283) withinthe repeating occurrence 1284 of fall events) when aggregated into aproperty channel create dipoles that have slope. Within this pointstream we may see that these points may be broken up into unidirectionalsegments. The first segment (A_(z), B_(z), C_(z)) has a positive slopeindicating that the magnitude of the fall event within the repeatingoccurrence 1284 is trending to be larger. These three points may beaggregated into an event depicted as occurrence Q 1296. This occurrencewill have its own properties (depicted as the attached circles L, M andN) according to the event definition of this occurrence channel. Thesecond segment (C_(z), D_(z)) does not qualify as an event (because ofduration requirements, for example). The third segment (D_(z), E_(z),F_(z)) does meet criteria and forms a second event depicted asoccurrence R 1298. The fourth segment (F_(z), G_(z)) does not meet eventcriteria. The fifth segment (G_(z), H_(z), I_(z)) meets criteria andforms a third event depicted as occurrence S 1300. Since these eventsidentified in the context of a property channel 1286 of a repeatingoccurrence 1284 are occurrences in their own right, they are aggregatedinto an occurrence channel 1288. Further, as in the example depicted,occurrences that repeat may be aggregated into repeating occurrences. Sooccurrences Q, R and S (1296, 1298 and 1300) may be aggregated into arepeating occurrence and the properties of the events may form aproperty channel 1290 which may be analyzed to find events (as event K1302 constructed from points Q_(M), R_(M), S_(M)) which may beaggregated into occurrence channels 1291. In other words, the processmay be recursively applied.

One species of repeating occurrence 432 is cycling. Cycling is aspecific type of repeating occurrence in which there is a repeatingoccurrence 4332 of a relational binary 462 made up of a perturbationevent is followed by an event returning the system to normal. Arepeating occurrence 432 with a property moving in a specific directionis called a trending recurrence. A trending recurrence is a specifictype of repeating occurrence 432, repeating occurrences 432 may betrending in more than one property. The relationship between occurrenceswithin a repeating occurrence supplies additional properties, which areplaced in a point stream and analyzed. For example, the time betweenoccurrences is tracked. As an example, consider 9 fall events that areaggregated into a repeating occurrence 432. In this case a propertychannel 442 of 8 points will be created of the time spans between theoccurrences within the aggregation.

The processor 304 may not create a definition for each repeatingoccurrence. A definition may be constructed from the minimum recurrencecount and recurrent thresholds supplied within the occurrence typedefinitions. The processor 304 allows repeating occurrence definitionsto be created to add additional criteria to the identification processand/or to add calculated properties and/or attributes to the repeatingoccurrence 432. In these cases where a specific definition is created,the researcher must indicate whether the user-defined definitionreplaces the default definition, or is to be identified in addition tothe default definition. In this way, the researcher may identify anynumber of specific types of repeating occurrences 432 for a singleoccurrence type 444 (e.g. oxygen fall event).

The researcher may also select to suppress repetition for any occurrencetype 444 causing the processor 304 to ignore repeating occurrences 432for the specified occurrence type 444. A repeating occurrence 432 isitself an occurrence and therefore may repeat. Recurrent repeatingoccurrences (which in themselves are simply repeating occurrences 432 ofrepeating occurrences 432) will create property channels for theproperties of the repeating occurrences 432 contained within. Forexample, the number of occurrences within a repeating occurrence will beaggregated into a property channel 442. As with the repeating occurrence432, the Recurrent repeating occurrence does not require a definition,but the researcher may provide a definition to add additional criteriaand/or specify calculated properties and/or attributes, repeatingoccurrences 432 and Recurrent repeating occurrences may be elements ofan image 452. In particular, the processor 304 recognizes a particulartype of image designated a deviation image. In a deviation image arepeating occurrence 432 is followed by a specific image or set ofimages that represent a deviation from an established cyclic phenomenon,narcotic-induced ventilation instability is one such deviation image. Inthis case, the physiologic response to an oxygen desaturation event hasset up a repeating occurrence 432 of convergent oxygen reciprocationbinaries. If this repeating occurrence 432 is followed by a narcotictherapy event and then a divergent oxygen reciprocation binary theprocessor 304 will identify the narcotic-induced ventilation instabilitydeviation image. In one embodiment, deviation images include additionalfundamental properties including the point of deviation—the time atwhich the repeating occurrence ended.

repeating occurrences 432 may be identified for any occurrence typesincluding images 452, therefore many repeating patterns of occurrencemay be addressed with this mechanism. For example, if A and B areimages, then the pattern ABABAB may be identified as the repeatingoccurrence of an image defined as AB. Patterns that are more complicatedthan simple repetition are addressed in the processor 304 with thepattern occurrence 446. The pattern occurrence 446 defines a pattern interms of other occurrences. Researchers first define the set ofoccurrence types which will be used at least once in the pattern anddefine each a mnemonic. For example, the researcher may choose 3 imagesand assign them the mnemonics A, B and C. Mnemonics are alphanumericrepresentations of occurrences used as a reference to an occurrencetype. Once the set of occurrence types has been selected and mnemonicsassigned the researcher may define the pattern in terms of the mnemonicsseparating them with brackets. For example, the researcher may define apattern as [A][B][A][C][A][B][A]. In one embodiment, a patternoccurrence 446 may have a set of patterns, any of which, if matched,will cause a pattern occurrence to be created, pattern occurrences 446are defined as a subclass of repeating occurrence 432 and therefore haveproperty channels 442 in the same way that repeating occurrences 432have property channels 442, but, since the members of the patternoccurrence 446 are of different types, the property channels are onlycreated for properties that match within the pattern occurrence. Forexample, in the example above (with the pattern [A][B][A][C][A][B][A]),there will be property channels 442 containing 7 points for propertiesthat exist in A, B and C. For properties that exist only in A and B,there will be property channels with 6 points, pattern occurrences 446are occurrences themselves and therefore the processor 304 willautomatically identify repeating pattern occurrences as described above.

When the processor 304 identifies an occurrence 448 (or candidateoccurrence) the aggregation of all elements within that occurrencebecomes a micro-domain. Relationships, properties and elements withinthis micro-domain have meaning because they exist within the specificcontext of the occurrence. The processor 304 provides for mechanisms toexploit this fact and to allow the researcher to specify properties thatreflect the new meaning of properties and the relationship betweenelements within the micro-domain of a specific occurrence type 444. Whenan occurrence 448 is identified it has a set of properties 456 or valuesthat the processor 304 identifies, calculates and/or assigns. There arethree types of properties 456—fundamental properties 468, calculatedproperties 470 and attributes 472. Fundamental properties 468 aredefined by the processor 304 depending on the occurrence mode and thenature of the associated point stream or occurrence stream. For example,all occurrences have the fundamental properties of start time, end timeand duration to name a few. As another example, all occurrences that areevents 474 on a numeric point stream have a magnitude and slope to namea few. As another example, repeating occurrences 432 have a member countand mean separation duration to name a few.

Calculated properties 470 and attributes 472 are properties 456 thathave an expression-based definition. The processor 304 uses thismechanism to refine processor 304-specific occurrence objects andelements (as an example Inflection points 486). The researcher is alsoable to define these properties and store these definitions with theoccurrence definition (See FIG. 7A). The results of these expressionsare attached to the occurrence 448 and available within the matrix.

FIG. 6 depicts a UML static diagram of a subset of classes within theprocessor 304 specifically modeling the relational binaries resultantfrom an analysis, may According to one embodiment, there are threesubclasses of relational binaries 500; the convergent binary 506, thedivergent binary 508 and a null binary 504. (Although others relationalbinaries may be provided). These three subclasses do not represent threedifferent types of binaries with different binary definitions, butrather the 3 forms (call binary Mode) that a binary created from asingle binary definition may take.

In summary the three modes are described as follows:

-   -   1. convergent binary—the mode in which the Alpha and Beta        occurrences were identified as expected    -   2. divergent binary—the mode in which the Alpha occurrence was        identified but the Beta element was not found as expected    -   3. null binary—the mode in which the Alpha occurrence was        identified but the time series (or set of time series) in which        the Beta element was specified is missing, corrupted or        otherwise unavailable.

The three modes are described below in more detail. A convergent binary506 represents a relational binary wherein the processor 304 hasidentified the Beta occurrence which has been defined as having anexpected relationship to the Alpha occurrence. A convergent binary 506may have either a true occurrence 512 or a missing occurrence 514 as abeta depending on what has been specified as the expected condition. Ifa true occurrence 512 was specified in the relational binary definition(See FIG. 8b ) then the associated convergent binary 506 may have a trueoccurrence 512 as a Beta. If a missing occurrence 514 was specified thenthe associated convergent binary 506 may have a missing occurrence 514as a Beta. The class structure therefore allows for zero or oneoccurrence 512 and zero or one missing occurrence 514. In a presentlycontemplated embodiment a convergent binary 506 may not contain two Betaoccurrences (missing or True).

divergent binaries 508 represent a pair of occurrences identified in arelationship that contradicts the expected relationship as described inthe binary definition. Therefore a divergent binary 508 may have eithera true occurrence 512 or a missing occurrence 514 as a Beta depending onwhat has been specified as the expected condition. If a True occurrence512 was specified in the binary definition then the associated divergentbinary 508 may have a missing occurrence 514 as a Beta. If a missingoccurrence 514 was specified then the associated divergent binary 508may have a True occurrence 512 as a Beta. The class structure thereforeallows for zero or one occurrence 512 and zero or one missing occurrence514. According to one embodiment, a divergent binary 508 may not containtwo Beta occurrences.

Null binaries 504 represent the existence of a condition in which analpha occurrence was identified but the data stream (or stream s) fromwhich the expected beta event is to be derived is unavailable to theprocessor 304. A missing occurrence 514 is associated with a set of timeseries segments 518 that represents the areas within the matrix that wassearched for the occurrence described as expected in the binarydefinition. Null occurrences 510 are not associated with time seriessegments 518 because at least one time series to which they would havebeen attached or the relevant section of that time series is unavailableor corrupted. The processor 304 will convert null binaries 504 toconvergent 506 or divergent 508 binaries as new time series or timeseries segments become available and analysis is executed. An occurrence512 may be the Beta occurrence of a first relational binary 500 and theAlpha occurrence of second relational binary 500.

In one embodiment occurrence stream s are stored as divergent binarystreams and convergent binary streams. In the alterative or incombination, all such stream s or a portion of specific stream s or agrouping of stream s filtered for severity of divergence (for example)may be aggregated and rendered for periodic viewing wherein, forexample, the temporal relationships of for example divergent binaries orof the occurring images are easily recognized or specifically indicated.

To further clarify this structure it may be useful to describe the orderof operation within an example of a embodiment of the processor 304 asit constructs the analysis according to one embodiment.

-   -   1. The processor 304 selects a relational binary type for which        the dependencies have been shown to be available; then    -   2. The time series that are associated with the relational        binary definition are iterated through to match any identified        occurrences 512 with candidate Alpha occurrences (as defined in        the specified binary definition). A single occurrence 512 may        match any number of Alpha occurrence definitions and each one is        considered a candidate Alpha occurrence.        -   a. For each candidate Alpha occurrence, the specified search            region is examined for the expected Beta occurrence            -   i. If any of the time series in which the expected Beta                occurrence is unavailable or corrupted                -   1. A null binary 504 is created (along with its                    associated Null occurrence 510)        -   b. If the expected Beta is located and the specified            conditions obtain            -   i. A convergent occurrence 506 is created        -   c. If the expected Beta is not located or the specified            conditions do not obtain            -   i. A divergent binary is created 508

The actual relationship between the alpha and beta events, which includethe object binaries, is not necessarily defined by cause and effect(which may not be known with complete certainty) but is rather definedby the pattern relationship such as a temporal, spatial, and/orfrequency relationship of the occurrences, or simply by their priordesignation as a relational pair. For example, the actual relationshipbetween the alpha and beta occurrences including a given relationalbinary could be a cause and effect, two effects resulting from anunmonitored cause, a relationship between two monitoring technologiesmeasuring the same physiological phenomenon, an expected compensatoryresponse, or a pathologic response, to name a few. Once the generalassociation has been captured within the binary then the relationshipbetween the occurrences within the binary is examined to determine ifthe relationship meets the requirements for the determination of ahigher level of relationship (e.g. cause and effect, compensatoryresponse to name a few). Though the focus of FIG. 6 is relationalbinaries 500, it also depicts the structure of images 502. The processor304 creates images by aggregating dynamic binaries 500.

Specifically, the search for an image may be reduced to successivesearches for occurrence binaries. An image is defined by a set ofoccurrences and their relationships within the image. Two sets ofoccurrence sets are defined—Sequenced and Non-Sequenced. Sequencedoccurrences are defined with a time relationship to at least one otheroccurrence within the image. Non-Sequenced occurrences are defined onlywith a time relationship to the span of the overall image. (See FIG. 8cfor a complete description of the definition).

The process of searching for an image starts with making sure that allof the constituent occurrences types have already been processed forconstruction and the resultant occurrence stream s have been placed intothe matrix. At this point the overall search region (a single patientstay, for example) is reviews to gather the counts of the constituentoccurrences within the region. If any of the required occurrences has acount of zero within the target region then the search for the specificimage type may be abandoned. (In an alternative embodiment, theprocessor 304 continues to construct the image as much as possible so asto store “Near Miss” images for research or analysis purposes.) Next theSequenced set of occurrences is processed first. Sequenced occurrenceshave a specified time relationship to at least one other occurrence typewithin the image and therefore may easily be aggregated into dynamicbinaries 500. For example, if three occurrence types are specified as A,B and C; A is defined as preceding B and B is defined as preceding Cthen the processor 304 may aggregate this set of 3 occurrence types into2 dynamic binaries to search for. The first dynamic binary is created bychoosing A as the Alpha occurrence and B as an expected Beta occurrence.This dynamic binary is searched for within the target region and theresultant dynamic occurrence stream is placed back into the matrix. Oncethe [A→B] dynamic binary has been processed, then the second dynamicbinary may be created as a binary having the dynamic [A→B] binary as theAlpha and the C occurrence type as the Beta (described as [[A→B]→C]. Ifonly the time relationship has been specified between the constituentsequenced occurrences (as opposed to, say specific related propertieswithin the created micro-domain), then the order of the dynamicaggregation of binaries is not required to follow the sequence. In otherwords, the processor 304 may, for performance or other reasons, chooseto aggregate B and C first as [B→C] and then aggregate A as [A→[B→C]).Since the process of searching for binary occurrence is equivalent forsearching for a single occurrence (when only the time relationship isspecified) then the results are the same. In this way, any number ofsequenced occurrence types may be processed to find their existencewithin the target time region.

non-sequenced occurrence types are searched somewhat differently becauseof their unstructured nature. First the occurrence counts of allspecified non-sequenced occurrence types are placed into a set andsorted from least to greatest as far as their count within the targetregion. In an example of a embodiment the processor 304 begins with theleast count occurrences and proceeds through greater count occurrencesto maximize performance. For each occurrence type within thenon-sequenced set a dynamic binary is created first with the resultantoccurrence created by aggregating all of the Sequenced occurrence types(as [A→[B→C]] in the above example) and then with all of the otherNon-Sequenced occurrence types. Continuing the example above, if twoNon-Sequenced occurrence types are specified D and E, then the followingset of dynamic binaries would be created and processed: [D→[A→[B→C]]],[D→E], and [E→[A→[B→C]]]), [E→D]. In certain cases, the reverse dynamicbinaries (both [D→E] and [E→D] are not required.

An image may also specify that a specific occurrence must not be foundproximate to the rest of the image. In this case processor 304 will usethe verify non-existence relationship within the dynamic binariescreated. If multiple occurrences of a dynamic binary are found duringthe image identification process the processor 304 will select which ofthe occurrences to include into the image. (In one embodiment, prunedconstituent occurrences are stored for research or analysis purposes.)The selection may be directed by researcher specifications so as tominimize time relationships within the image or may be directed tomaximize the overall span of the image 434. Dependencies may also be afactor. For example, if a more proximate occurrence being selecteddestroys a dynamic binary required for image completion the processor304 may select a different occurrence to be maintained though it is lessproximate. This decision may be directed by the researcher according tothe nature of the micro-domain and its relationship to physiologicalforces.

Occurrence stream s created from dynamic binary types will be maintainedwithin the matrix if the target image is found. If the target image isnot found, the processor 304 may be configured to remove the dynamicbinary occurrence stream s since they are relevant only within thecontext of the given image micro-domain, image Identification iscompleted if and when all of the dynamic binaries that are createdgenerate occurrences that match the required time relationships. Whenthis occurs the constituent occurrences are aggregated into amicro-domain establishing the scope of the image. At this point furtherstages of construction may proceed to generate properties, relationshipand sub-elements, refine scope and finally to qualify the image to seeif it is a true image or a disqualified candidate.

FIG. 7A is a UML static diagram depicting a subset of classes within theprocessor 304 specifically modeling the occurrence definition set 530used by the processor 304 to identify, construct and qualifyoccurrences. An occurrence definition set 530 is an aggregation ofoccurrence definitions 544. In one exemplary embodiment, the processor304 applies an occurrence definition set to a set of raw point streamsto create the objectified time-series matrix. An occurrence definition544 is metadata used to identify, construct and qualify an occurrencewithin the matrix. Once an occurrence has been created it is linkedthrough its occurrence type 542 to the specific occurrence definitionfrom which it was created. Every occurrence definition contains fouraggregations of sub-elements: property definitions 540, Synonyms 538.Qualification Rules 546 and Dependencies 548, properties definitions 540provide the ability, once a micro-domain scope has been established, torefine, quantify, highlight and further express the nature of themicro-domain. There are three types of properties: fundamentalproperties, calculated properties and attributes (as described in FIG.5). Fundamental properties are defined by the processor 304 and may ormay not have an explicit definition in the occurrence definition set.Calculated properties and attributes will have a definition in theoccurrence definition set associated with a specific occurrence type.

In one embodiment, a researcher may specify an inheritance relationshipbetween occurrence types. With this mechanism, all of the propertiesassociated with the parent occurrence type will be defined in the childtype. In this way, an “Is-A” relationship may be established. Calculatedproperties definitions are a type of property definition 540 definedwith a name and an expression. The expression may contain references toany number of other properties 456 (Fundamental properties, Calculatedproperties or attributes). This mechanism may be used simply to renamecertain fundamental properties within the context of the occurrencedefinition 544. For example, if a binary type called an oxygenreciprocation binary is defined as a fall event (Alpha) and a Rise event(Beta), then the oxygen reciprocation binary may include a calculatedproperty called Magnitude Ratio which is defined as“=Alpha.Magnitude/Beta.Magnitude”. The processor 304 verifies submittedexpressions for validity. Calculated properties have access within theirexpressions to a set of functions. Functions available includemathematical functions (Absolute Value. Square Root, to name a few),aggregation functions (Mean, Count, Sum, to name a few) and relationalconversion functions (Relative To, time Span From, to name a few) toname a few.

In one embodiment, to assist with cross-channel expressions theprocessor 304 provides functions and values that may be included inthese expressions. The Relative To function provides the ability toderive a percentage relative to a target element. Further, the processor304 allows evaluations relative to the ranges of associated time series.This value is called Normalized and has a specific function. Theprocessor 304 and researcher may create additional properties calledattributes using an attribute definition, attributes are properties thatare assigned based on condition being true, attribute definitions havethree parts; Conditional Expression, attribute Name and ValueExpression. If the Conditional Expression is found to be true then theValue Expression is evaluated and the result is assigned to the propertywith the name provided in the attribute Name. The Value Expression is ofthe same format as the calculated property expression. The ConditionalExpression uses the format of the calculated property expression butalso requires at least one Boolean operator (e.g. “alpha.Magnitude>4) orproperty.

In an alternative embodiment. Calculated properties and attributes maybe defined and/or assigned with an embedded programming language (e.g. ascripting language). In one embodiment, to assist with expressions, theprocessor 304 provides a mechanism to define Synonyms 538 within theoccurrence definition 544. Both elements and properties may be definedas a Synonym. For example an oxygen reciprocation binary may contain aSynonym called Recovery which is defined as “=Beta” and another Synonymmay be called desaturation and be defined as “=Alpha”. Synonyms aresimply symbolic replacements for use within expressions, conditions andsearch criteria. The processor 304 will pre-process all expressions toreplace synonyms before evaluation. For example, now the Magnitude Ratioabove could be defined as “Recovery.Magnitude/desaturation.Magnitude”.In one embodiment each occurrence must go through a qualification stageto determine its veracity as an occurrence of the type specified. Thisqualification process uses Qualification Rules 546 that are attached tothe occurrence definition. In one embodiment the subsequent stage ofqualification is accomplished through a rule set applied to theproperties within the candidate occurrence. In an alternativeembodiment, the candidate is run through a series of preservation andrejection tests. Preservation and rejection tests may be designated asabsolute (e.g. if this state obtains, reject the candidate with nofurther evaluation) or may be aggregated into a set of overrides. In thesecond case the rules are created in the format: “Preserve if X unlessY” and/or “Reject if M unless N” where X, Y, M and N may represent anyBoolean expression over the occurrence candidate's properties.

In the contemplated embodiment, the processor 304 maintains (or is ableto derive) the Dependency 548 of all occurrence definitions 544,property definitions 540 and Qualification Rules 548. This dependencyworks down to the Fundamental property level. The processor 304 maythen, given these dependencies, determine whether certain Fundamentalproperties. Calculated properties and/or attributes may be leftunevaluated or their evaluation delayed. For example, if an attribute iscreated by the researcher but not used (directly or indirectly) then theprocessor 304 will leave the attribute unevaluated. If at some futuretime, for example during display by the patient safety visualizationprocessor, this attribute is requested then the processor 304 willexecute evaluation. The management of dependencies also allows forcaching strategies in which the processor 304 stores the results ofevaluation during execution and/or persistence, but may accuratelyrecognize the need for re-evaluation if any of the dependent propertieshave been changed. Further, within the instance of an occurrence, notonly are the dependencies well understood, but the values of eachelement within the dependencies are known. Given this fact, theresearcher or care worker may drill down into occurrence instances tounderstand both the metadata and data dependencies of the selectedoccurrence. For example, within a oxygen Cluster (for example, definedas a repeating occurrence of oxygen reciprocation binaries), thecaseworker may see not only the value of a specified property (forexample, an instability index), the structure of calculation thatcreated the property and the specific occurrences and the values oftheir properties that participated in the final value.

Finally, an occurrence definition is associated with a set of orderedConstruction Phases 532. Elements within the occurrence definition 544may be associated with a specific Construction Phase to indicate to theprocessor 304 when it is appropriate to identify, create or evaluate thesub-element. For example, if constituent occurrence types are markedwith the Construction Phase “Scope”, then the marked occurrence typesare required for establishing the occurrence as a candidate occurrence.If they are marked with a subsequent phase then the constituentoccurrence is added after scoping to refine the scope, properties markedwith a specific Scope are not evaluated until the specified phase isbeing executed. Qualification Rules are automatically considered in theQualification Construction Phase.

Occurrence definitions 544 are subclassed into a set of definitionsclasses that represent the type of patterns that the processor 304 maysearch for. Specifically, occurrence definition 544 is subclassed intoevent definition 552, relational binary definition 554, image definition556 and repeating occurrence definition 536. The repeating occurrencedefinition is further subclassed into pattern occurrence definition. Therelational binary definition is subclassed into event binary definitionand occurrence binary definition (for more detail see FIG. 8B). Theevent definition is further subclassed as well (see FIG. 8A).

FIG. 7B depicts a model of the occurrence types 568 within the processor304. There are 5 primary occurrence types: event type 571, binary type572, image type 573, repeating occurrence type 569 and patternoccurrence type. For more description of the types as they relate tooccurrences within the matrix see FIG. 5 and FIG. 6. For moredescriptions of the types as the relate to occurrence definitions withinthe occurrence definition set see FIGS. 7A, 8A, 8B and 8C. FIG. 8A showsa subset of the occurrence definition set as it relates to the eventdefinitions. In one embodiment, the classes described here represent theevent definition set used by the time series objectification processorto create the event streams.

An event definition 584 is a subclass of an occurrence definition 582and each event definition is associated with a unique event type 586,event subclasses are described in detail in the text associated withFIG. 5. The definitions of each of these event subclasses are containedwithin the directional event definition 588, threshold violationdefinition 590, state transition definition 592, and state matchdefinition 594 respectively, state transition definitions 592 and statematch definitions use state sets and therefore contain state setdefinitions 596 which provide the parameters, lists, expressions orother mechanisms for defining a distinct aggregations of states, eventsare occurrences that happen within a single time series and thereforethe event definition 584 is associated with a single time series type580.

FIG. 8B shows a subset of the occurrence definition set as it relates tothe binary definitions. In one embodiment, the classes described hererepresent the binary definition set used by the Relational Processor tocreate the convergence analysis. A relational binary definition 612represents the parameters used to identify a relational binary. In oneexemplary embodiment, a relational binary definition 612 is made up offour key elements—the binary type 614, the Search definition 620 and thedefinitions of the Alpha and the expected Beta.

There are two subclasses of relational binaries—event binaries andoccurrence binaries. The event binary definition 616 requires that theAlpha and Beta are events and therefore uses event definitions 622whereas the occurrence binary may use an occurrence definition 624 asAlpha and Beta, relational binaries that are between an event and anoccurrence will be an occurrence binary since an event is a subclass ofoccurrence. A Search definition 620 includes parameters, expressions andother descriptions to tell the processor 304 the relative time seriessegments) to search for the Beta event or occurrence. The Search Mode626 indicates different types of searches including Expected. VerifyNon-Existence and Reoccurring Verification. A Search definition 620 musthave at least one Search Mode 626 but may have more that are used incombination (e.g. a Reoccurring Verification of Non-Existence). For eachrelational binary, the relationships may be further defined to select amore specific Relationship type 610 (or to match one of a set ofrelationships), for example cause and effect, subordination to name afew. The relational binary definition may optionally include rules forthe indication of a specific Relationship type. In an alternativeembodiment the selection of the Relationship type is implemented throughthe attribute mechanism.

This structure may best be understood within the context of the UserInterface modeled in FIG. 9. FIG. 9 shows a set of event binaries. Eachpair of events (e.g. 744, 748), which has a connecting relationship(e.g. 764), represents a single event binary definition 616. Theconnecting line between the two events represents the Search Mode 626(of FIG. 8b ). Search Modes include: Expected 716, Analogous binary 720,Verify Non-Existence 728, and Reoccurring Verification 732 to name afew. The Search Mode 626 determines the type and frequency of searchthat may occur when the candidate Alpha event is identified. Forexample, the Reoccurring Verification 732 type may generate multiplerelational binaries for a single candidate Alpha occurrence because itdirects the relational binary processor to search for the Expectedoccurrence with a specified frequency, generating relational binaries ateach interval. In the contemplated embodiment some binary Search Modesmay be used in combination (e.g. Reoccurring Verification 732 and VerifyNon-Existence 728).

The box containing a pair of time offsets (e.g. 768) represents theSearch definition 620. This definition contains the Start and End timeoffsets from the end point of the Alpha occurrence for which the Betaoccurrence should be searched in the target Beta time series. Finallythe icons represent the Alpha and Beta event types. These typesreference a unique definition which provides the parameters with whichthe relational binary processor may search the identified time seriessegment(s) for the existence of a pattern. Further, criteria iscontained within the binary definition itself based on the micro-domainestablished by a candidate aggregation of an alpha and beta paired bythe spacial requirements alone. These criteria, established by theresearcher or automatically determined by the processor 304 (as perguided image discovery described below), may utilize all of theproperties (including Fundamental properties. Calculated properties andattributes described below to name a few) of the candidate binary toaccept or reject the binary as a true representative of the specifiedbinary type.

FIG. 8c shows a subset of the occurrence definition set as it relates tothe image definitions. In one embodiment, the classes described hererepresent the image definition set used by the image processor to createthe image analysis. An image definition 642 represents the set ofelement definitions and their relationships, which allow the imageprocessor to determine whether the pattern of elements meets thecriteria of the specified image. This Structure of FIG. 8c may best beunderstood within the context of the User Interface in FIG. 10. Eachdiagram represents a single image definition 642. If a specific sequenceof elements is required to identify the image then the sequence isspecified with connectors and time offsets (e.g. 812, 824, 816, 828, and820). Each icon represents an occurrence type which may be used toidentify a single occurrence definition 646, image elements may includean Instance Mode (644 and 648) which indicates the Mode of the specificinstance to be found. For example if the occurrence is a binary then theMode indicates whether the binary should be a convergent, divergent ornull binary. Within FIG. 10, the mode is specified by in a parentheticaldescription at the end of the occurrence type name. Icons may representany occurrence type—event types, binary types, image types, repeatingoccurrence types or pattern occurrence types, occurrence types arelisted 815 and available for selection, drag-and-drop. Relationships, aswith the binary editor are available 833 to establish the relationshipand/or search mode between sequenced elements.

FIG. 9 shows an embodiment of a convergence editor, which, in oneembodiment, provides the ability for the creation, and modification of abinary definition set, which may be used by the relational binaryprocessor to create the convergence analysis. A binary definition setmay be represented as a convergence model—a visual representation of theobject instances shown in FIG. 8b . The user interface includes a designsurface 764 and an element toolbox 700, which allows for thedrag-and-drop creation and manipulation of a subset of the convergencemodel called a binary diagram. The aggregation of all binary diagramscreated with a single name constitutes the entire convergence model andmay be, in one embodiment, persisted as a binary definition set in therelational database, in an XML file, a model or in DSL artifacts (eithertextual or visual) to name a few. Breaking a convergence model intobinary diagrams allows for multiple views into the model. These viewsare not mutually exclusive (i.e. the same binary definition may berepresented in multiple diagrams) and therefore provide views into modelat various levels of complexity and points of reference.

FIG. 9 provides a reference example to describe the elements within theconvergence editor and the relationship to the elements in FIG. 8b ,convergence Element Toolbox 700 presents the visual elements which maybe added to the design surface and therefore to the binary diagram. Theicons represent occurrence types that may be added. The Relationships768 section of the toolbox 700 presents a set of lines, which may beused to connect two events to create a relational binary. The linechosen determines the Search Mode 626. Search Modes include: Expected716. Analogous binary 720. Verify Non-Existence 728, and ReoccurringVerification 732. The visual icon attached to the line cues the user toits mode. The Search Mode 626 determines the type and frequency ofsearch that may occur when the candidate Alpha occurrence is identified.For example, the Reoccurring Verification type 732 may generate multiplebinaries for a single candidate Alpha occurrence because it directs therelational binary processor to search for the Beta occurrence with aspecified frequency, generating binaries at each interval. Some SearchModes may be used in combination (e.g. Reoccurring Verification 732 andVerify Non-Existence 724).

Each relationship added to the design surface 764 must have at least onetime interval provided (e.g. 768) which represents the Search definition620 for the relational binary definition 612. Each relationship may bedirectional. The line includes an arrow end-style on the end thatrepresents the Beta definition, either a Beta event definition 622 or aBeta occurrence definition 624. The end without an arrow represents theAlpha definition, either the Alpha event definition 622 or the Alphaoccurrence definition 624. Within FIG. 9, all of the binaries definedare event binaries.

Each pair of events, which has a connecting relationship, represents asingle event binary definition 616. In the above figure, the followingseven binaries:

-   -   1. An Analogous binary between Nasal Pressure fall and oxygen        fall (736, 772, 740)    -   2. An Expected binary between oxygen fall and oxygen rise (740,        773, 748)    -   3. An Expected binary between oxygen Floor Breech threshold        violation and oxygen rise (744, 768, 748)    -   4. An Expected binary between oxygen rise and oxygen fall (748,        770, 740)    -   5. An Analogous binary between oxygen rise and Nasal Pressure        Rise (748, 774, 752)    -   6. A Verify Non-Existence binary between oxygen fall and Pulse        Rise (740, 771, 756)    -   7. A Verify Non-Existence binary between oxygen fall and Pulse        fall (740, 769, 760)

This diagram does not represent all of the relationships of each ofthese events. It is an example of a subset view into the overallconvergence model with a focus on sleep apnea. Relationships andelements may be removed from this diagram without removing them from theentire model (i.e. the editor distinguishes between “Remove” whichremoves the element from the diagram but not the model and “Delete”which removes the element from the diagram and the model [including allother diagrams]). A diagram may be constructed that shows all of theevents and relationships, but it would likely be so large and complex asto be unreadable.

The editor will check the diagram for validity before persistence or atthe user's request. For example, a relationship without a Betaoccurrence would invalidate a diagram. An invalid diagram may invalidatethe convergence model. It is contemplated that a convergence modelcannot be persisted into a binary definition set. The editor allows foran invalid state to provide flexibility during diagram construction.Further, if the target binary definition set is associated with imagedefinition sets that are available to the editor, the editor may warn ofconflicts with associated models by changes to the diagram. Depending oneditor settings, these changes are disallowed, or the changes may bepropagated into the images. Each diagram element may be manipulated in amore detailed way through property editors associated with the elementtype and access to the occurrence property Subsystem described below.The property editors provide access to all editable properties of theassociated definition objects such that the editor is sufficient toconstruct a complete binary definition set. The editor provides foradding text, notes, lines and other visual elements to the diagram toincrease human readability and to communicate between users. Theseadditional visual elements have no affect on the binary definition set.

FIG. 10 shows an embodiment of the image editor that provides theability for the creation and modification of an image definition set,which will be used by the image processor, in coordination with a binarydefinition set, to create a convergence analysis. A image definition setmay be represented as an image diagram—a visual representation of theobject instances shown in FIG. 8. The user interface includes a designsurface 832 and an element toolbox 780, which allows for thedrag-and-drop creation and manipulation of a subset of the image calledan image diagram. The aggregation of all image diagrams created with asingle name constitutes the entire image model and may be persisted asan image definition set in the relational database, in an XML file, amodel or DSL artifact (either textual or visual) to name a few. As withthe convergence model, image diagrams are views into the model thatprovide visualizations at various levels of complexity and points ofreference.

FIG. 10 provides a reference example to describe the elements within theimage editor and the relationship to the elements in FIG. 8C. The box onthe left is the occurrence type Selection Box 815 which presents thevisual elements which may be added to the design surface 832 andtherefore to the image diagram. The design surface is split into twosections—Sequenced and Non-Sequenced. If there are only elements in oneor the other then only the one section is show (as in FIG. 10 in whichthere are only Sequenced occurrence types specified), occurrence typesdropped into the Sequenced section require a relationship in time andtherefore require that a relationship be specified between them (e.g.824). The Relationships Selection Box 833 presents a set of lines, whichmay be used to connect two occurrence types. Each relationship added tothe design surface must have a time interval provided (e.g. 828) whichrepresents the Search definition 642 associated with the SequencedInstance Mode 644. Each relationship is directional indicatingprecedence in the sequence. may

Zero or more sequences may be specified, but if an element is placed inthe Sequenced section it must be part of a sequence. Elements placed inthe Non-Sequenced section cannot have relationships. Only existence isspecified within an overlapping Span of Influence (defined below). Theimage diagram differs from the binary diagram in that the diagram itselfrepresents an entity—the image definition 650—and is not simply acollection of other entities (e.g. binaries in the case of the binaryeditor). Removing elements changes the definition of when a image willbe identified. All elements added to the image diagram represent an“And” relationship for identification purposes (i.e. all elements andsequences must exist for the image to be identified). In one embodiment,to create “Or” scenarios, multiple image diagrams are created withvariation representing the “Or” combinations. An image may include anycombination and number of occurrence types. The editor may check thediagram for validity before persistence or at the user's request. Theeditor allows for an invalid state to provide flexibility during diagramconstruction. Each diagram element may be manipulated in a more detailedway through property editors associated with the element type. Theproperty editors provide access to all editable properties of theassociated definition objects such that the editor is sufficient toconstruct a complete image definition set.

The spatial configuration (in time) of the occurrences are preferablysatisfied to create a candidate image. Once a candidate image isestablished, the processor 304 may use this set of objects as amicro-domain to establish all of the properties using the occurrenceproperty Subsystem (described below). The properties derived within thismicro-domain may then be used to refine the definition of an image, tomore specifically characterize the image or be used in the decision toaccept or reject the candidate image as a true (i.e. qualified) image.The image editor may be used to enter into the occurrence propertySubsystem to further define calculated properties and attributes(defined below) of the image. The editor provides for adding text,notes, lines and other visual elements to the diagram to increase humanreadability and to communicate between users. These additional visualelements have no affect on the image definition set.

FIG. 11 provides an additional example of a binary diagram referring toheparin therapy in which the following binary definitions are specified:

-   -   1. A Reoccurring Verification binary 854 between heparin therapy        850 and PTT Rise to Therapeutic Range 858.    -   2. A Verify Non-Existence binary 866 between heparin therapy 850        and Pulse Rise 862.    -   3. A Verify Non-Existence binary 882 between heparin therapy 850        and Blood Pressure fall 870.    -   4. A Verify Non-Existence binary 886 between heparin therapy 850        and Hemoglobin fall 874.    -   5. A Verify Non-Existence binary 890 between heparin therapy 850        and Platelet Count fall 878.    -   6. and other examples

FIG. 12 provides an additional example of a binary diagram referring toinsulin therapy in which the following binary definitions are specified:

-   -   1. An Expected binary 922 between insulin therapy 920 and Blood        Sugar fall 924 To Therapeutic Range.    -   2. A Verify Non-Existence binary 926 between insulin therapy 920        and Blood Sugar Breech 930.    -   3. A Verify Non-Existence binary 926 between insulin therapy 920        and Confusion 928.

FIG. 13 provides an additional example of a binary diagram referring tonarcotic therapy in which the following binary definitions arespecified:

-   -   1. A Reoccurring Verification binary 944 between narcotic        therapy 940 and Pain Score fall To Therapeutic Range (948)    -   2. A Verify Non-Existence binary 952 between narcotic therapy        940 and SPO₂ cycling 956.    -   3. A Verify Non-Existence binary 960 between narcotic therapy        940 and Blood Pressure fall 961.    -   4. A Verify Non-Existence binary 962 between narcotic therapy        940 and Respiratory Rate fall 964.    -   5. A Verify Non-Existence binary 966 between narcotic therapy        940 and Contusion 967.

FIG. 14 provides an additional example of the image editor in whichthree non-sequenced binaries (969, 970, and 971) are defined assufficient to identify possible Heparin-Associated Hemorrhage.

FIG. 15A shows an image frame 973 of a patient's physiologic system andcare and demonstrates one exemplary image according to one embodiment asgenerated by the image processor. The image shown is indicative ofdynamic progression from an image suggestive of stability to an imagesuggestive of a cascade of septic shock. The image displays objectifiedevents, which met criteria as up and down arrows indicating whether theyare, rise events or fall events respectively. Minor time seriesvariations (such as detected minor rises or falls typical of signalnoise, which fail to meet criteria by the objectification processor asevents) are represented on each time-line as open circles along paralleltime lines. (The visualization of such variations may be turned on oroff as desired.) The detected events are combined with other events toform binaries which are then combined to produce an image of relationalpatterns including aggregate binaries and individual events defining thedynamic state of the patient's physiological system and of the medicalcare applied to the physiologic system during the time interval of eachrespective image Within the complete image, smaller images aggregate toproduce the larger image of failure (in this case, of septic shock).

Since FIG. 15A is a late “time lapsed” frame of a MPPC, which hasexhibited many earlier frames, wherein the processor 304 suggested thatconfidence of septic shock was high. The figure is readily understood bythe representations of rise events or fall events as up-arrowheads anddown-arrowheads respectively on each time line 974, each of which islabeled on the left. The timelines 974 are grouped into categories 975designated on the right. The first event detected within the timeinterval of the image is a perturbation event—a rise event of theNeutrophil count 976 shown by the upward pointing arrowhead on theNeutrophil timeline. This perturbation event is combined by therelational processor to a second perturbation event—a rise inrespiratory rate 977 also shown by an upward arrowhead, to generate thefirst relational binary 978 (combined in the figure by the arrowconnecting 976 and 977). (While the respiratory (tidal or ventilationrate) may be used the respiratory amplitude (tidal or ventilationamplitude) may alternatively be used or a mathematical combination ofboth may be used to generate a time series and/or a derivative of thetidal curve (in one example the slope and amplitude, the area under thepeak to peak and/or the area above the nadir to nadir) may be used, timeseries of all of these may be incorporated into the matrix for at riskpatients or the time series may include only one or two but expanded toinclude derivatives retrospectively and prospectively upon the detectionof a pattern or image or upon the identification of risk factors. In oneembodiment the respiratory time series are monitored using a nasalcannula whereas in another they are monitored using a sound sensorplaced on an airway or chest. Both the rate of the tidal sounds and theamplitude of the tidal sound and the length of the tidal sounds in eachcycle can be used to provide an indication of tidal amplitude. Earlytermination of tidal sounds (especially inspiration) before the nextbreath suggests that the tidal amplitude is not high. The duration ofthe tidal sounds and the tidal sound amplitude can be used in a manneranalogous to the duration and amplitude of the nasal thermistertemperature in a single direction.) Each subsequent perturbation in theimage is designated by its timeline and arrowhead. An arrowhead with acircle around it designates perturbations determined by testingautomatically ordered by the processor 304 in response to the detectionof a particular image. In an example the rise event in inflammatorymediators or indicators 979 was ordered by the processor 304 to betterdefine the inflammation portion of the image which was somewhat obscuredbecause the early images demonstrated a rise in neutrophil count, a risein pulse, and a rise in respiration rate but with a normal temperature.Since this ambiguous image must be better defined to decide care,testing for inflammatory mediators/indicators is automatically orderedby the processor to better complete the image.

Using these basic designations the image of FIG. 15A becomesself-explanatory and FIG. 15A reveals a clear image frame (a time lapsedsnap shot) late an MPPC including perturbations of inflammation,followed by a hemodynamic perturbations, followed closely by respiratoryperturbations, and then renal perturbations in an expanding and linkedcascade 980. Note that the initial rise in Neutrophil count 976, thefirst detected perturbation event, will have completely disappearedlater in the cascade such that frames late in a failure process are bestviewed with the sufficient scale to observe the onset of the cascade980. Note the image shows a complete lack of any events along thetemperature timeline 981. Without the Patient safety processor, the lackof a fever could easily fool a healthcare worker who may think of feveras a reliable indicator for the early detection of sepsis. Note howeverthat the processor 304 is programmed to recognize that it has renderedan incomplete image and the processor 304 seeks to complete the image byordering testing for inflammatory mediator 979. This testing selves as a“surrogate images” for a rise in temperature thereby establishing thatthe entire image does in fact exhibit an early component ofinflammation.

Two drug treatments are evident in the image, the antibiotics Vancomycin982, designated by its dose on the time line, and Levofloxacin 983,similarly designated. Also a rise in IV fluids in the form of normalsaline 984 is indicated. All of these treatments come late after theimage has long been indicative of a high probability of sepsis. (Thisdelay, which may be detected in real-time by the patient safetyprocessor, suggests poor and ineffective care, which has ignored orotherwise been poorly responsive to the patient safety processor. Theprocessor may be programmed to provide an indication of the quality ofthe care provided, time lines, which include the care worker or ward maybe provided so that delays may be linked to particular locations or careworkers.)

The image of the progressive cascade 980 shows the drug treatmentscomponents 982, 983 of the image are too late because they appear withinthe image very late along the cascade 980. The late portions of theimage of the cascade 980 also include a very ominous beta including arise in anion gap 985. The addition of this new image provides a matureimage of cascade 980, which is now strongly indicative of a stage ofseptic shock. Other image views may be for example; specific expandedportions of the time lines, specific expanded views of images (or otheroccurrences) along the timeline portions, specific groupings of thetimelines, overviews of perturbation progression from group to group (anexample of this is shown in FIG. 19), to name a few.

FIG. 15B is the image frame of FIG. 15A with portions of the image beingseparated into sequential states of inflammation 986, systemicinflammatory response syndrome 987, presumptive severe sepsis 988, andpresumptive severe septic shock 989.

FIG. 15C is an early image frame from real time imaging of the processin FIG. 15. The first “spark”, a rise in Neutrophil count 990 evident inthis image, is entirely non-specific despite the fact that it, inretrospect, heralds the onset of septic shock, completely disappears bythe time this motion picture has reached the point illustrated in FIG.15D focused testing, more frequent CBC testing, and/or more frequentvital sign measurement to determine the significance of this rise inNeutrophil count may be suggested or ordered by the processor to expandthe image to more quickly move toward a more specific image.

FIG. 15D is an image frame from real time imaging of the process in FIG.15. This frame demonstrates early images of inflammatory, hemodynamic,and respiratory augmentation 991 combined with early immune failure 992.

FIG. 15E is an image frame from real time imaging of the process in FIG.15A This frame demonstrates demonstrate the images of inflammatory,hemodynamic, and respiratory augmentation 991, with immune failure 992,but now with images indicative of a decline in respiratory gas exchange993 and fall in platelet count 994.

FIG. 15F is an image frame of FIG. 15A to demonstrate that the image ofnow shows expansion of the image of the failure cascade from the framein FIG. 15E to now include the images of metabolic failure 995, renalfailure 996, hemodynamic failure 997 and respiratory failure 998. Thisis the point wherein rescue begins in many patients monitored by today'sEMR and monitoring systems. The introduction of fluid resuscitation 999at this late frame of the image means that the fluid will likely havelittle effect on progression of the image.

FIG. 16 shows a time lapsed image frame of the failure cascade ofcongestive heart failure. Note the first perturbation event detected bythe processor is hemodynamic (a rise event in pulse rate 100), ratherthan inflammatory as in FIG. 15A. Then the next detected perturbationevent is respiratory, a rise in respiratory rate 102 which combined withthe rise in pulse 100 produces the first relational binary 104. Notealso there is a fall in the ventilation indexed oximetry value 106producing a second relational binary 108 with the rise in respiratoryrate 102. The rise in respiration rate 102 is the beta event of thefirst relational binary 104 and the alpha event of the second relationalbinary 108. Together these two joined relational binaries form an image110, which may be followed back to the initial onset of the image of thenascent congestive heart failure cascade 112. Treatments includingfurosemide 114 and metoprolol 116 are initiated fairly close to theonset of the image of the nascent cascade 112 but are not effective inpreventing subsequent occurrence of an image of a progressive cascade118. This image of a progressive cascade 118 is constrained by the boththe components and length of the MPPC. The Patient safety processor upondetection of this image may search for the fundamental cause of thecascade progression, as by automatically ordering cardiac enzymes (notshown), and other tests if the safety committee of the hospital desiresthis type of testing proactivity in this setting. Note the cascade 118includes the development of atrial fibrillation 120 and subsequentfurther deterioration.

FIG. 17 shows an image frame of sleep apnea. Note the first perturbationevents occur in a group including a repeating occurrence of eventswithin the pulse channel 122, respiratory channel 124, SPO₂ 126, andpulse upstroke channel 128. These occur after the initiation of anarcotic dose of 3 mg IV 130. The aggregated images showing cycling, aspecific species of repeating occurrence. 132 then repeats to producesecond such images 133 and third such images 134. The SPO₂ cycle 135portion of the third images showing cycling 134 becomes more severe withrecovery failure 136. CPAP treatment 137 is given timely and no furthernarcotic is given. Note, in this case, there is no image of an expandingcascade or progressively declining respiratory rate or declining SPO₂ toindicate life-threatening narcotic induced sustained hypoventilation. Onlater review as in morning report or with teaching rounds the entireMPPC, which contain this frame, may be reviewed by moving along a fastframed image to better visualize the subtleties of the progression.Furthermore the physician or nursing group may drill down to see thatactual time series (as, for example, by right clicking on the SPO₂repeating occurrence symbol 137). The decision as to whether or not thetreatment in this case rendered timely care may be assessed. In anexample, the physicians in the session may petition the patient safetycommittee to adjust the processor 304 to provide a recommendation forearlier automatic RT department notification, along with the nursenotification when images such as those defined in the early portion ofthis motion picture are present. In this way the Patient safetyprocessor becomes an integral part of the continuous quality improvementactions of the hospital system with the goal being to move treatment andtesting leftward into the earliest frame, which provides sufficientimage support for the treatment or testing. The goal is to a continuingmove toward earlier treatment of the source of the early perturbationsbefore the cascade develops. According to one aspect, the processor 304is integrated into the continuous quality improvement process and theprocessor 304 becomes an integral part of the hospitals qualityimprovement committee meetings and a major source of hospital wide aswell as focused analysis and a mechanism to rapidly institutionalizequality improvement focused change.

FIG. 18 shows an image frame indicative of a high confidence ofthrombotic thrombocytopenic purpura (TTP) a rare thrombotic andinflammatory condition that mimics the image of septic shock. TTP may becaused by the inhibition of ADAMTS enzyme by autoantibodies but thisdisease may also be rarely triggered by the very common drugclopidogrel. TIP often occurs within 2 weeks of drug initiation and mayresult in complications if not detected.

Unfortunately. TTP shares many of systemic response features of the verycommon disorder of sepsis (FIG. 15), which also causes thrombocytopenia.Since sepsis is a much more common condition, misdiagnosis of sepsis inthe presence of TTP is a high possibility: furthermore, as with mostpathophysiologic failures, both processes may coexist in a singlepatient along with other related conditions such as systemic lupuserythematosis and pancreatitis. Despite the fact that the moving imagesof failure in TTP and sepsis are similar, misdiagnosis of sepsis in thepresence of TTP may be serious since TTP may not respond to antibiotictreatment.

Since TTP is associated with the accumulation of large multimers of VonWillebrand factor which damaging red blood cells and induce extensivemicro vessel thrombosis producing confusion, renal failure andmicroangiopathic anemia which is associated with sentinel schizocyteswhich may be detected in the peripheral smear of blood (if the diagnosisis suspected and the test is ordered). Thrombocytopenia, renal failure,and hematuria may appear earlier in this process than with sepsis butthese early findings are only an “image clue” and does not differentiatethe two moving images. The decision to diagnosis a rare conditioninstead of a common one on the basis of a clue is a dangerous humantendency and a pitfall, which may result in patient complications.Alternatively the decision to diagnosis a common condition despite theclue because as the trite medical student saying goes “common conditionoccurs commonly” is equally dangerous. Indeed it is tragic that patienthave to die because of such trite and oversimplified thinking. Howeverit is the nature of many humans often approaches the analysis ofoverwhelming complexity with unknowingly capricious, summary judgment.This combined with the overlapping complexity of disease and healthcareis one of the most important reasons that comprehensive real-timephysiologic and care rendering by the generation of digital MPPC andcare is important.

The MPPC suggestive of TTP may be generated by the processor, with theprocessor indicating a image consistent with the possibility of sepsisand/or TTP and other less likely conditions such as an acutevasculidity. The processor may output non-specific characterizations ofthe image such as “image consistent with a life threatening acute orsub-acute thrombotic and inflammatory augmentation” and may present adifferential diagnosis of the processes, which may generate such animage.

Also, as for example upon the detection of a threshold frame or frames,automatically order the peripheral smear, blood cultures, urinecultures, sputum cultures. Chest X-Ray, ANA, pancreatic enzymes, renalsediment, and ANCA study to enlarge and fill in the gaps of the image asrapidly as possible. It is the hospital experts who will ultimatelydecide the cost effective balance of ordering these tests as defined bythe position the tests are ordered along the cascade. If desired thereports from the Chest X-Ray may include a section which will appear asa time series (as for example a step function). The radiologist in theinterpretation (and in comparison with the last test and the last numberselected by the last reading radiologist) may enter an indication ofpulmonary infiltrate, pulmonary edema, and the like and may indicate avalue between 1 and 5 which may result in a step change of the processor304 from the last test. In this way the results of studies such as ChestX-Rays and other such interpreted tests become a source for dynamic timeseries rendering and incorporation into the imaging process. This willalso provide an objective tool for comparing subjective quantificationbetween radiologists and between various testing modalities in relationto the actual MPPC thereby identifying radiologists who are notgenerating reasonably reproducible or comparable subjectivequantification in relation to themselves, others or the MPPC. In anexample if a radiologist consistently calls the level of pulmonary edemaa 1 or 2 in patients who have MPPCs consistent with of acute severe CHFand acute severe pulmonary edema or if the radiologists quantificationconsistently fails to follow or predict the clinical course theninstruction can be provided or in the alternatively it can be recognizedby the processor 304 that the input from that specific radiologist isnot useful in further defining the images along the processor 304.

The presence of an image including images defining a failure cascade1108 including inflammatory—hemodynamic respiratory—augmentation 1116with an early fall in platelet count 1104, a fall in the Ventilationoximetry Index (VIO) 144, a fall or threshold value of hemoglobin 1144,an rise or threshold value of a confusion score 1148, and/or a rise orthreshold value of red blood cells in the urine 1150, and/or a rise orthreshold value of Creatinine 1152. Together the combination of imagesproduces a MPPC suggestive of the possibility of TTP and/or sepsisand/or other less common processes. For example, if the patient had justreceived blood it would suggest a possible transfusion reaction.

It is not as important for the processor 304 to make the diagnosis as itis for the processor 304 to indicate to the healthcare worker thegravity of the image, a differential diagnosis as suggested by theimage, and the general type and/or physiologic description of failurecascade present, and perhaps a notification that the detection by theprocessor 304 of this type of image requires prompt notification of theattending physician and transfer to ICU. If the image has insufficientbinaries because results are not available to define enough betacomponents to define the presence of the image suggestive of TTP with asufficient confidence level to take action, the unavailable tests areordered upon the detection of the partial image in an attempt tocomplete the image. Note in FIG. 18, the detection of the imagessuggestive of the possible presence of a complete MPPC of TTP triggeredthe test for Schizocytes 1161 in an attempt to complete the TTP image.The detection of a threshold value step function, and/or rise inschizocytes combined with the rest of the image triggers the warning ofthe potential presence of TTP. In FIG. 18 reflects poor care because theaction based on the processor 304's order for plasmaphoresis 1162 isphysically carried out too late. This delay is automatically detected asis the outcome and the processor may be configured to provide anautomatic report of variance to the quality improvement department ofthe hospital.

In this case, failure to rescue is not preempted because of human delayin physically following the orders of the processor 304. The delay incarrying out the order is determined by the processor 304 and theprocessor 304 may be programmed to up-indicate the warning uponincreasing delay. To prevent this delay, the processor 304 may beprogrammed notify another station if action is not taken in response todetection of various evolving images such as the one in FIG. 18. Thesemay be decided for example by the hospital quality improvementcommittees or by individual physicians or nurses if desired so that theprocessor 304 improves over time and may be adjusted to compensate forthe diligence of the healthcare worker. The patient receivesLevofloxacin early to cover the possibility of sepsis as the image wasalso consistent with sepsis and the healthcare workers decided toempirically treat for sepsis (albeit with somewhat limited antibioticcoverage). However, the cascade proceeds despite antibiotic therapy.Since a cascade is an image and the relationship of the cascade, itsgrowth, and its features and its timing within an MPPC in relation tothe dose, timing, and type of treatment also forms part of the MPPC,these relationships may be automatically assessed by the processor inreal-time to determine if treatment is effective. The hospital safetycommittee or infectious disease committee may decide whether or not toreprogram the processor 304 to make antibiotic suggestions based onvarious ranges of images before the results of cultures are known.

FIG. 19 shows an overview image of perturbation onset and progression asderived from the time lapsed MPPC of FIG. 15A wherein the perturbationsin each grouping are incorporated into an aggregate index along a singlesmoothed time series for each group. Note this is a typical progressionof sepsis with initial involvement of the inflammatory group 160 theneach other group is involved in progression. Note the late timing of thetreatment 162 is particularly evident in this summary view derived fromthe more complex images.

Rather than or in combination with an index, if desired the processormay be programmed to provide an indication of the severity and number ofthe aggregate perturbations in each group. These may be for exampledesignated by many enlarging or colored arrows, other icons, and/ortimed instability scores, to name a few. Many such options may beincluded so that the user may define his or her preference to visualizethe sequence and patterns of cascade progression across groups.

A range of expert and pattern recognition systems may be applied toanalyze the images and the images generated by the image processor.These include the image Identification Processor. In one embodiment theimage Identification Processor works with the image editor which allowsthe user to select the images for detection using for example a from adrag and drop interface. In an embodiment the drag and drop interfaceprovides for the discretionary selection of, for example, thetime-series type to be selected, then occurrences are selected on eachtime-series type in order and the ranges of relative positions andorders occurrences is selected. In this example, the image editor allowscustomization of the desired ranges for the components of the images(and therefore the ranges of the images themselves) to be selected aswell as the response of the image Identification Processor to thedetection of a given image and/or images. The image editor may allow forselecting the ranges of timing and order of the occurrences to generatea specific output such as a proposed diagnosis, warning, order for moretesting or imitation or termination of treatment. The imageIdentification Processor may also be adaptive such that a physicianinputs the diagnosis present, such as for example septic shock, with agiven image. The physician may also capture a given image or set ofimages into the image editor to then select ranges about the occurrenceswithin the image which also would have indicated the presence of septicshock so that the adaptive image processor may learn more quickly.

FIGS. 15, 16, 17, 18 and 20 represent a 2 dimensional “time lapsed”snapshot view four MPPC after they have proceeded to advance states.This view also provides an alternate user interface for the creation andediting of the image definition set. Researchers may use an image editorto create and manipulate image models such as those examples depicted inFIGS. 15, 16, 17 and 18.

In one embodiment researchers work from the top down to define images.Researchers begin by selecting a set of channels in which they want to“paint” the image. FIG. 20 depicts the image editor being used to“paint” the narcotic-induced ventilation instability image, channels(100, 102, 104, 106, 108) may be ordered in any number of ways, bysorting, categorizing or by simple drag-and-drop selection of locationwithin the image editor, channels may be duplicated (e.g. 100, 102, 104,106) to expand the image so that the relationships may be defined in anon-overlapping way for complex definitions that define multiplerelationships. The image editor maintains the relationships within andbetween defined elements within the channels regardless of theirvertical location within the editor. Researchers then select a channeland the image editor presents a set of occurrences that are availablewhich apply to the given channel. Researchers may select any of theseelements and drop them on channel. Also, the researcher may create a newelement at any point within a channel (for example using a right-clickmenu editor). Locations within the editor indicate relative locations intime between selected and/or created elements. If an occurrence whichspans multiple channels is dropped on a location, the image editordetermines the additional channels to be added. The location of thecorresponding event is determined as the midpoint of the search windowdefinition. The entire window is shown as a set of parenthesis 116indicating the range of the search window relative to the correspondingevent, in this case a treatment event with an IV narcotic 114. Searchwindows are shown only within the beta channel of the relational binaryand the event itself is show within the midpoint of the search window.If an event is both a beta and an alpha event the search windowdisplayed is around the event is specific to the event when it isparticipating as a beta event. Search windows may be suppressed withinthe editor and/or shown only within the relational binary currentlyselected due to the fact that a single event may be the beta of anynumber of binaries. Individual events may be dropped onto a channel orcreated on a channel. New event types may be defined within the imageeditor. Events may be connected with a drag-and-drop selection or withan alpha and beta click selection, for example to define new eventbinary types.

The image editor creates and modifies image definition sets.Furthermore, the image editor works in concert with both the convergenceeditor and the event editor to create and modify the binary and eventdefinition sets. In one embodiment (shown in FIG. 20), the definition ofimage is accomplished with a split-screen view showing the image editorin the top pane 118 while the image definition editor is in the lowerpane 120 showing an alternative type of image diagram. These two modelsare completely synchronized with changes in one immediately reflectingthe change in the other.

In one embodiment researchers work from the bottom up to define failuresfrom a set of time series. Researchers may begin with a set of actualtime series from patients diagnosed with known failures, with a set oftime series generated by the processor to simulate certain conditions ora set of time series simulating no perturbation at all within a patient.This set of time series may be designated as immutable (for example withthe set of actual time series) or may be edited to provide a sample ofthe patterns being defined. Researchers may select portions of the timeseries, which the image editor then will analyze to provide candidateevent definitions. Alternatively the researcher may select parameters todefine an event and the time series displayed will indicate the resultsof that definition overlaid on top of the time series to provide visualguidance to the researcher. Once the researcher completes the definitionof an event the image editor will compare that definition with otherdefinitions within the same channel. If similar patterns are found theresearcher is alerted and allowed to create a new event type or selectone of the event types already selected. If the event is a relationalevent, the researcher may select a corresponding event from whichrelational parameters may be defined and experimented with or theresearcher may simply define a function (e.g. >2×Relative Magnitude).Once an event has been fully defined then the researcher may choose torelate the event to another event within the image or to a search windowwithin the image (e.g. to indicate a missing or null event). Theresearcher may indicate that a processor-ordered event as the beta of arelational binary. Groups of events and relational binaries or any otheroccurrence may then be selected to define a images, images alreadydefined within the image definition set are highlighted such that theymay be included into the image the researcher is working with or theresearcher may simply select to alter its definition. Access into theoccurrence property Subsystem is available and the expression editorsincluded indicate immediate results with respect to the current image orother occurrences selected. This allows the image editor to work on allaspects of the image including scope definition, qualification rules toname a few.

In one embodiment, the image editor may be presented with a largecollection of time series sets provided with the indication of thepresence or absence of a particular known image. The image editorcreates a set of candidate definition sets refining them to create theright specificity and sensitively to match the sample set. Once thebest-fit definition sets are created, a second large collection of timesseries sets are provided with the indication of the presence or absenceof a particular known image. The image editor first uses the candidatedefinition set, determining sensitivity and specificity, and thenrefines the definition set to be better suited if possible to both thefirst and the second collection of sample data. This process may beexecuted iteratively until a best-fit set of definition sets is createdor the process is deemed not to be asymptotic and is abandoned.

In one embodiment the image may be “played” or executed by the imageeditor as an MPPC to provide further time-specific markers. A defaultexecution of an image is “played” by placing all events as specified intheir default (e.g. midpoint) location within their respective searchwindows as defined by the image definition. A sample result of this isdisplayed in FIG. 15B. Once the image is played vertical markers areplaced within the timeline as in FIG. 15B to indicate progressive stateswithin an evolving image. In this way, the image definition may beprovided the specifications by which the image state may be identifiedand displayed within the Patient Safety Monitor. FIGS. 15C,15D,15E and15F show the 4 views of an image evolving within the Patient SafetyMonitor over time. The Patient safety processor identifies one or moreof the diseases, disorders, or cascades which are most consistent withthe present state of the image and displays it at the bottom of themonitor (along with differential diagnosis if desired).

In an alternate and/or complimentary embodiment, the image editorprovides the ability to split the execution of an image into multipleintermediate and/or end states. Each different branch within the imagedefinition may be defined as a state within an image or a different,albeit related, image. Trees of related images may be composed toprovide alternative evolutions of failure within the image definition.

FIG. 22 is a frame from a time lapsed motion image including a pluralityof timelines from the patient illustrated by the dotted lines in thefailure mode diagram of FIG. 1. In this image the patient who hasexperienced a stroke has now developed a condition associated with seruminappropriate antidiuretic hormone (SIADH), which induces an inducedfall in serum sodium and confusion. On detection of the elevatedconfusion score processor 304 examines the pathways for confusion (seesome example in FIG. 1) and does not find a low SPO₂ low or highrespiratory rate, a high ventilation oximetry index, or a risinginflammatory (although not shown here, in addition to systemicinflammation (for example due to sepsis) the processor 304 may alsocheck for focal inflammation such as intra cavitary inflammation of thebladder as a cause of the confusion. The search for a metabolicoccurrence is positive with the detection of hyponatremia. This ofcourse does not mean that this is the cause (and the processor 304 willwarn that the stroke may still be the direct cause) but the processor304 cannot make the common mistake that the stroke is the cause and stopthere.

The case presented in FIG. 22 is similar to a recent case, which wasevaluated in consultation by one of the present inventors. To show howthis type of problem happens every day in the hospital and toparticularly to demonstrate the long unfulfilled need, the history ofthat case will be discussed in greater detail.

The patient presented with an acute stroke but was recovering and alert.Then he slowly began to develop confusion and less alertness. As thestroke was large the nurses and physicians managing the case thoughtthat the patient's confusion and obtundation was due to brain swellingand called the family in to adjust code status. For this reason thefamily consulted one of the present inventors. The patient SPO₂ andventilation rate were normal, he had no signs of sepsis and because ofrecently normal electrolytes the attending physicians did not think thata metabolic cause for the confusion was a reasonable option. In otherwords they misdiagnosed the pathophysiologic failure pathway(illustrated on the failure mode diagram 200 of FIG. 1) and they thoughtthe pathophysiologic pathway was following the direct connecting line170 between stroke 208 and confusion 220 as shown in the failure modediagram 200 in FIG. 1. However, prior to the onset of the confusion thepatient was receiving 0.5 NS in spite of the fact that that he waseating and drinking. Repeat serum sodium confirmed a fall in sodium andSIADH was confirmed with additional testing. Cautious correction of hissodium resulted in rapid recovery and resolution of the confusion andobtundation. This might be considered a straightforward case inisolation but it shows how easy it is to go down the wrong path whenmanaging many complex patients while trying to remember how alert andactive such patient were the previous day. Subtle symptoms sneak up onhealthcare workers and delay detection of life threatening failures.

Since the stroke caused the SIADH (which cased the fall in serum sodium)the actual modes of failure were significantly different than suspectedby the hospitalist in this case. The actual failure followed to path 171from the stroke 208 to the hyponatremia 242 and then followed the path172 from the hyponatremia 242 to the confusion 220. In this case thepatient survived the missed diagnosis but he experienced several extradays unnecessary days in the hospital because of delay in detection andtreatment of this failure.

Now referring to FIG. 22 note that this image is derived from a patientwith the failure mode diagram of FIG. 1 having a timeline for a stroke180, diabetes 181, atrial fibrillation 182, a history of congestiveheart failure 183, and sleep apnea 184. These correspond to the failuremode diagram of FIG. 1 illustrating potential relationships betweenstroke 208, diabetes 202, atrial fibrillation 206, congestive heartfailure 204, and sleep apnea 210. Note that in FIG. 20 the Patientsafety processor is ordering routine confusion scores 192 because of thetimeline 180 indicating a stroke. The detection of an increase inconfusion 185 or the presence of hypotonic saline administration 186 toa patient with a stroke timeline 180 automatically triggers a measure ofelectrolytes and glucose 187 and upon the detection of a fall in serumsodium 188 the processor orders a urine osmolarity 189 and indicates ahigh probability of SIADH 190 and recommends an adjustment in fluidtherapy 191.

Here the problem is simple but the early signs of failure were at firstsubtle at a time when intervention would have prevented the increasedlength of stay later the pathways of failure were confused leading tofurther delay and considerable family since they were told the incorrectdiagnosis. In this case the nurses and physicians may have been busy ormay have been inexperienced or simply not familiar with the subtledecline in mutation, which may attend the development of SIADH in astroke patient. The reason subtly findings are missed is myriad. Notealso, in defense of the healthcare team, as illustrated in the failuremode diagram of FIG. 1, this is simply one failure and there are verymany potential failures for this complex patient and all the nurse andphysicians are caring for many such patients. Furthermore, in this casethe serum sodium was nearly normal when the low sodium was finallydetected so many physicians would not think the level was sufficientlylow to cause these symptoms or warrant intervention. However, the sodiumhad dropped from a high normal to just below normal and in patient withbrain edema the magnitude of the fall in serum sodium may be moresignificant than the absolute value and this variation in vulnerabilityfrom patient to patient and within the same patient depending oncoexisting disorders, diseases, and medications are not concepts whichare easily grasped by some healthcare workers who have observed patientswith very low serum sodium values without any change in mentation. Thisillustrates the value of generating and recognizing a moving picture ofthe failure and care. The Patient safety processor does not need to seea threshold breach because it is looking at the entire failure and careimage over time and, it is programmed to recognize that this imageindicates vulnerability to a fall in serum sodium, even a fall whichdoes not go below threshold. The Patient safety processor provides theadvantage of continued vigilance and continuous consideration of all ofthe potential physiologic failures, which are consistent with theimages.

According to one aspect of the present embodiment failure mode diagrams,such as the one in FIG. 1, may be used to construct images by applyingthe cascading binary relationships between diseases, treatments, andperturbations to construct images and image ranges using the imageeditor. According to one embodiment a failure mode diagram designer isderived for use, for example, by each hospital department or by hospitalexpert groups to generate failure mode diagrams which relate to theirpatient populations. In one embodiment of the failure mode diagramdesigner, a drag and drop tool is provided for entering and/or selectingdiagnosis, treatment, complications of disease, complications oftreatment, actions of treatment, outputs of monitor, and erroneousoutputs and/or failures of monitors (to name a few). The failure modediagram may include icons for the drag and drop editor. The failure modediagram may be built in an interactive programming environment whichallows the reader to quickly zoom in on any region and the explore andvisualize the diagram by diagnosis, complications, drug, treatment, andthe like. In this way the interactive effects of any drug can beinstantly visualized in relation to its potentially positive or negativeeffects in different disease states and different failures. This failuremode diagram can be used to assist in the programming of the processor304 and the processor 304 can provide outputs in the form of highlightsalong the failure mode diagram to indicate the potential failuresdetected. In this regard FIG. 1 and FIG. 22 are two exemplary outputs ofthe processor 304 but they are also exemplary views of editors, whichcan be deployed to program the processor 304 for failure mode detection.

FIG. 21 is an image frame of an image editor for constructing workingwithin a range of the MPPC for the recognition by the Patient safetyprocessor of images and other occurrences associated with a specifiedcondition. In this case the image shown is consistent with andindicative of presumptive severe sepsis. Note theinflammatory/hemodynamic/respiratory augmentation 192 is followed in theimage by a fall in VIO 193 and metabolic failure with a rise in aniongap 194. Note that if the inflammatory/hemodynamic/respiratoryaugmentation 192 is unassociated with a rise in temperature (a nullbinary 193 is identified), inflammatory markers 195 are ordered toconfirm the presence of the inflammatory component of the image. Thetypical sequence of binaries is shown but the parentheses 196 indicatethat in these events may occur in any order. The processor 304 mayprovide greater confidence if the order is as shown and lesserconfidence if the order is different that shown. As noted images mayoverlap such that patient with preexisting hemodynamic instability maybecome septic, for this reason, in this case the order is not deemedpivotal. However, for some images the order of events may provide muchgreater specificity (in which case the parenthesis is adjustingaccordingly). At first the image editor may be set to be more liberaland then adjusted as hospital experience and the quality improvementdepartment dictates.

The patient safety processor is not constrained by these definitionsduring analysis in an absolute way, but rather compares actual data to aplurality of images and image states to find best-fit matches. Thepatient safety processor will indicate all possible images and imagestates ranked by level of confidence. For example the patient safetyprocessor may indicate that a MPPC is consistent the SystemicInflammatory Response Syndrome with a high degree of confidence andearly septic shock with a medium degree of confidence and that TTP (andother potential alternatives) or overlapping failure modes are remotelypossible in view of the image and remain to be excluded. The physicianmay be asked if it is desired to order the focused testing to excludethese remote alternatives or overlaps and/or the processor may beprogrammed to automatically add this testing based on a specific rangeof images (as defined, for example, using the drag-and-drop editordiscussed previously).

The identification of failure within the patient safety processor is notthe single selection of a failure mode or a failure state, but theranking of a set of images with regard to their fit within the datapresented. The identification of multiple images is not simply theselection of alternatives. Multiple failures may, in fact, exist and beinteracting with each other. Early states of some images may be verysimilar, or in fact exactly the same, as the early stages of otherimages or of a combination of images. The patient safety processorprovides the analysis and visualizations that allow the health worker tounderstand the current state of the patient (and patient environment) interms of possible future states—alternatives and candidateoverlaps—along with confidence levels as to their specification.Further, the Patient safety monitor allows the health care worker toquery the patient safety processor with regards to confidence levelsand, in particular, the comparative confidence level between two imagesand/or image states. For example, the confidence level for sepsis is lowwith the frame shown in FIG. 15B whereas it is intermediate for fame inFIG. 15C and high form all later frames. These confidence levels alongwith the action desired may be programmed into the processor 304 inadvance by specialty groups, hospital safety committees, and/or may becustomized and “tuned” by individual physicians and or may be appliedadaptively by the processor by comparing the entered new diagnosis withthe present image and recoding that image as indication of that state.In the adaptive mode the processor may be programmed to ask “is thisimage indicative of a failure process defined by this newly entereddiagnosis and, if so, please specify the first event, binary or imagewhich in retrospect was part of this specific failure process.”

In one embodiment, the patient safety processor may be trained by apathophysiological engine (such as a human simulator as are known in theart) for the creation of failure and response images. Given a specifiedevent definition set and binary definition set the Patent SafetyProcessor provides a dynamic image derived from the input of thepathophysiologic engine and the Processor is instructed as to the natureof the images so that when these images are detected in the future theyare recognized. In one embodiment a human simulator is connected to theprocessor 304 to provide an improved teaching tool for healthcareworkers. Researchers may select to be presented with a normal,unperturbed patient with various conditions. Once a dynamic image of thepatient is displayed researchers may introduce perturbation into thepathophysiological engine, which will result in new dynamic images fromthe Patient safety processor. For example, a research may selectrelationships presented according to a convergence and toggle them todivergence. Also, random divergence may be configured into the system.Divergence with respect to a single or a set of response system(s) maybe specified to model the breakdown of systemic response. Divergence maybe configure globally or for a specific timeframe indicating thatsystemic response fails, or is delayed. In this way both perturbationand failure of systemic response may be selectively introduced to createimages. These images may be persisted to be further edited within theimage editor or other tool. The researcher may select several differentvariations and save them as failures and/or failure states. Thesefailures and/or failure states may be persisted within a imagedefinition set to be used by the image processor. Further, resultantimages may be compared with actual patient data to refine image, binaryand event definition sets.

Alternately or in combination, according to one embodiment, an MPPC maybe carried from the processor 304 to the processor driving the humansimulator so that healthcare workers may observe the reanimation of theMPPC of the processor 304 either as a digital animation or as areanimation derived from output of a human manikin.

One utilization of the embodiment, which combines the pathophysiologicalengine to the processor 304, is to model treatment protocols. The enginemay output expected or unexpected parameters (divergence) in response totreatment and the image output of the processor 304 may be observed,and/or recorded for protocol modeling. Further, using the ability tointroduce divergence, allows processed protocols or other protocols tobe verified for reasonable redundancy to cover failures of systemicresponse.

One of the problems with conventional diagnostic process is that, evenwith diligence, the time to detection of the cause of a complex processis a direct function of care worker experience. One of the presentinventors, a trained critical care physician, has over many yearsmanaged perhaps several hundred septic shock patients and for thisreason he learned to visualize the complex cascades embodied in FIG. 15in when reviewing the patients chart in consultation. However, this isnot an easy task and requires vast experience. Yet during this time thenumber of TTP cases evaluated by this physician was low. Therefore eventhe most experienced physicians have only limited experience withuncommon disease cascades. To solve this problem, as shown in FIG. 23, apatient safety processor international network 10 in each hospital 12 isconnected to its own processor 14 and each processor 14 is connected toa central MPPC archive 16. The MPPCs from each processor 14 are uploadedto the central MPPC archive 16 from each hospital. The central MPPCarchive is connected to the international processor 18, which serves toprocess the MPPC from the central MPPC archive 16 and to improve MPPCrecognition and to develop new image and failure mode recognition andtreatment protocols. MPPC from a hospital processor 14, which areclassified, as associated with an objectively known case, such as a MPPCsuggestive of pulmonary embolism including a positive pulmonaryangiogram are applied to build an objectively defined MPPC database tofurther build the scope and specificity of the MPPC of pulmonaryembolism. In the alternative MPPC which are classified as associatedwith an subjective final diagnosis, such as a MPPC suggestive of SLEinduced alveolar hemorrhage, for example, (followed by a opinion of aconsensus group that this was the final diagnosis) is added to thesubjectively defined MPPC database case database to further build thescope and specificity of the MPPC of SLE induced alveolar hemorrhage. Inthis way a massive database may be derived from MPPC and images, whichare components of MPPC, derived from the worldwide management ofdisease. International testing and treatment protocols based on thereal-time MPPC detection may be developed which may potentially set aminimum standard of detection of catastrophic events even in ruralhospitals with a few beds, in urban hospitals which are poorly staffed,and in environments wherein physician and nurse experience may be verylow. New protocols may be derived and uploaded to these hospitals fortheir discretionary use as analysis of the MPPC results in response toolder protocols or new or additional treatment outside the protocolsreveals potential for improvement. The approach has the potential toprovide improved surveillance of drug reactions and efficacy, after, forexample the introduction of a new drug into a protocol, which may be anexperimental protocol, missing portion of the MPPC may also beidentified to support the development of new tests, which fill in thegaps or perhaps reduce the number of tests required to define cause(s)of the failure. Cost comparison of different testing and treatmentprotocols may be performed.

The bandwidth of the MPPC is defined by the number of tests, historicdata, and treatments et al, which include the MPPC. When potentiallytreacherous images of perturbation are identified along the MPPC thePatient safety processor is programmed to quickly broaden the bandwidthto investigate the alternative causes. This is important because thelonger the duration an undetected failure mode the greater the increasein cost and mortality because complications develop with widen thecascade and make salvage more expensive and difficult.

A narrow bandwidth (fewer tests and/or simpler tests per unit of time)is, on the other hand. (without considering the cost of allowing alonger duration of failure) less expensive than a broader bandwidth. The“effective bandwidth” includes those components of the bandwidth, whichactually contribute to characterize the factors actively defining theimages, which are components of the MPPC. Poorly conceived testing andtreatment increases the bandwidth and the medical cost but may notincrease the effective bandwidth. The goal of the processor 304 is toincrease the effective bandwidth as rapidly as possible withoutbroadening the bandwidth inordinately. The ideal system monitors with afew monitors and tests but uses these as sentinels, increasing thenumber of monitors and tests automatically if specified occurrences areidentified or failure cascades begin.

Therefore, one function of the present invention provide a mechanism toautomatically increase the effective bandwidth of the MPPC at any time(for example at 2 AM in a rural hospital), to optimally shorten theduration of failure without the application of a continuously wide andexpensive bandwidth. One optimal mechanism to broaden bandwidth is withimproved testing, such as focused tests, which have a high sensitivityand specificity for a specific failure mode. The MPPC archive 16 of thepatient safety processor Network 10 may be examined for opportunities toincrease the motion picture bandwidth and achieving a balanced mechanismfor mortality and cost reduction by shortening the duration of failurethrough earlier detection and improved treatment response.

The use of smaller bandwidths of monitoring and testing combined withthe ability to auto adjust to a wider bandwidth upon detection ofevents, binaries, images, repeating occurrences and patternedoccurrences reduces cost, and allows application to a larger populationsof patients for the same or less cost. The occurrences, which precedephysiologic failure, are recorded and this relationship is monitored bythe interaction processor 304 via the network. If a relationship ofphysiologic failure to these specific occurrences is identified, theprocessor further examines the MPPC for these now sentinel occurrences,which, if obtained earlier using a wider testing bandwidth, might haveprovided a more diagnostic image earlier. These test are then added tothe processor 304 as automatically ordered upon the detection of the inthe sentinel occurrences. If a test or set of tests fails to provideadditional image detection or quantification, which is not provided byanother cheaper or less invasive test or set of tests, then the test orset of test is not used. In this way the processor 304 is auto adaptive,learning to optimize the processing of the MPPC by eliminatingunnecessary or redundant testing and by adding testing which allowsearlier detection of failure thereby reducing cost as a function offacilitating timely prevention of cascade development or prevention.

This same approach, using the adaptive processor 304 communicating withone or more centralized processor 304 s over the national orinternational network to continuously or intermittently adjust andimprove the testing components of the MPPC may also be applied to thetreatment components of the MPPC to determine evidence that a giventreatment had a measurable effect (positive or negative) on the MPPC.Treatment components include, for example, drugs, fluids, nutrition,surgical treatments, inhaled gas treatments, pressure treatments,rehabilitation, exercise, positioning, splinting, to name a few. TheMPPC provides the images of the treatment relationships, includingrelationships related to timing of the treatment order, treatmentdelivery, treatment dose or procedure, the pattern of the individualtreatment dose, the pattern of dose administration over time, the methodof administration, and other images for comparison with the images ofthe rest of the MPPC. New treatments such as new drugs, different doses,different procedures, and/or the elimination of a treatment may beapplied using the processor 304 to determine, as by the application ofstatistical comparison, the effect of the new drug on the MPPC inrelation to the MPPC without the drug or treatment. In one embodiment,this is determined by the centralized processor 304, which then adjuststhe protocol to produce the most favorable treatment regimen asdetermined by the processor 304 (and after approval by the physiciangroup overseeing the processor 304). Locally at each hospital they maychoose to accept or refuse the uploaded change from the centralizedprocessor 304. In either case the subsequent MPPC of the refusinghospital may be compared by the processor 304 to the MPPC of thosehospitals which accepted the uploaded changes into their processor 304to determine if the change indeed had a positive, negative, or no impacton MPPC (including the expense components of the MPPC).

The use of one or more centralized processor 304 s in communication withlocal processor 304 s through a national or international Patient SafetyProcessing Network allows local healthcare delivery systems, hospitals,and even different floors to use different testing and treatmentprotocols. This diversification is a great strength of the systembecause the centralized processor has a very wide range of alternativeMPPCs to choose from in selecting, what it, (and the overseeingphysician experts of the centralized processor 304 if desired)determines is the optimal treatment and testing protocols from thediverse sets of MPPCs (which include a divers set of treatment andtesting images).

As discussed, according to one embodiment, the patient pafety processingnetwork includes a set of local processor 304 located at hospital wardor unit. The Local processor 304 under the direction of the healthcareworkers at that location. This allows the local healthcare workers tocontrol the treatment and testing protocols, and variation of thetesting bandwidth, deployed for the patient under their control. Thelocal attending physicians individually or as a group as well as thehospital pharmacists and nurses may prescribe these protocols though theuse of the Local processor 304. The local processor 304 recordsworker(s) (for example as a step time series or non-numeric time seriesin which a state transition event occurs when the physician, or nursefor example assumes responsibility and a different state transitionevent occurs when he or she is replaced by another. Alternatively, andpossibly in combination, a state match event may be used to indicate theduration for which the physician or nurse was on duty. Those caring forthe patient are therefore part of the Motion Picture of PhysiologicalCondition (MPPC). Protocols may be decided by a group or by anindividual physician caring for the patient. The extent to which aparticular healthcare worker or group is statistically or otherwiseassociated with favorable or unfavorable MPPC may be assessed by theprocessor. The protocol choices for the local processor 304 may be madethrough the use of pre prepared MPPC protocols as previously discussed.

The local processor 304 may recognize the physician time-series andadjust the protocols and MPPC to match those selected by this physician.The physician may override the processor 304 and, if this occurs, thisoverride includes at least one event and includes a new time seriesuntil the override is withdrawn. The extent to which a particularoverride is statistically or otherwise associated with favorable orunfavorable MPPC may be assessed by the Hospital processor 304. TheHospital Group processor 304, or the International processor 304. Thesemay provide modifications in future protocols, and even provideincorporation of the modification of the override or the prevention ofthis type of override according to the MPPC associated with theoverride.

The local processor 304 throughout the hospital communicate with acentral “Hospital processor 304” which is preferably under direction ofthe quality improvement committee and the hospital experts in eachfield. The Hospital processor 304 communicates with all the Localprocessor 304 s and may be used to upload treatment/and or testingand/or bandwidth adjustment protocols and or comparison MPPC, which havebeen agreed upon for application hospital-wide to the local processor304 s.

Each Hospital processor 304 communicates with (and may be controlled by)central Organization processor 304. The healthcare “Organizationprocessor 304” allows standardization of the hospital protocols throughthe Hospital processor 304 s under its control to set minimum safetytreatment and testing standards and may be controlled by a centralizedquality assurance group with expert representatives from all of thehospitals. Since the individuals caring for the patient represent atleast one time series and the ward represents at least one time seriesand the hospital represents at least one time series and theorganization represents at least one time series. The MMPP at theOrganization processor 304 therefore includes all of these caseworkerand location time series for each patient. Alternatively, or incombination, if the Patient is wearing a monitored GPS unit, this mayinclude a location time series, which provides continuous real timelocation as part of the MMPP. (The processor 304 s may compare with theentered locations to the GPS location to produce a confirmatory binary.)

One embodiment demonstrates an example of how a new set of time seriesderived from a new test or testing device may be evaluated for costeffectiveness. In this example, a pulse oximetry reflectance probe ismounted (as by hat or headband or other fixation device one or both eyesto the patient's head) and the probe is wirelessly or otherwiseconnected to pulse oximeter and the local processor 304 (as by Bluetoothfor example). The transmitter may be mounted in the probe, or in or onthe headband, hat, or behind the ear (for example, in the position of ahearing aid if desired). A position sensor may also be provided mountedon the patient. A maneuver such as a change in body position from supineto standing may be detected and included as a state transition or statematch event by the processor 304 and a fall of a component of thephotoplethysmographic pulse (indicative of the perfusion of thecapillary bed distribution of the supra-orbital artery, a distal branchof the internal carotid) in relation to a maneuver to produce adivergent binary. In this way the flow of the capillary bed above theeye in relation to a standing maneuver becomes a surrogate maker ofother capillary beds supplied from the internal carotidin relation to astanding maneuver. Real time perfusion may be compared with that of theear, fingertip, or the pulse pressure (as by an invasive arterial linefor example) to identify disparate in perfusion in one or both of theinternal carotid distribution. The local processor 304 processes theMPPC with these as additional time-series. The local processor 304uploads the MPPC to the Hospital processor 304, and the Hospitalprocessor 304. Organizational processor 304, and/or Internationalprocessor 304 where the MPPCs may be evaluated using the patient safetycomparison processor to determine if, after adjusting for disparities inthe MPPCs as a function of co morbidities and the like, the MPPCs whichincludes orthostatic reductions in supraorbital perfusion (incombination with therapy or corrective action to reduce falls upon thedetection) is associated with a statistically significant decrease inthe number of falls in the hospital. These time series may beautomatically added (by automatically ordering the intermittent orcontinuous supra-orbital monitoring used the study) to increase thetesting bandwidth when it is detected that the MPPC of a given patientis similar to those of the study population (for example as by type ofsurgery, age, co morbidities, or other events or images) where theaddition of those processed time-series data had a positive impact onoutcome. If one the other hand disparate hypo-perfusion above one eye isidentified ultrasound evaluation for carotid disease may be ordered bythe processor 304 and the result added as an event.

All the Organization processor 304 s (or Hospital processor 304 s if thehospital is not under a central organization) are preferably connectedto an International processor 304. The International processor 304 ispreferably controlled by a healthcare information corporation, such asGoogle or Microsoft, which maintains the International processor 304 andthe network as part of a centralized repository of electronic medicalrecords for example Health Vault™ by Microsoft). Each subordinateprocessor 304 (below the international processor 304) is capable ofoperating independent of the processor 304 Network so extensiveredundancy, lack of subordinate dependency, and therefore greater safetyagainst network failure is built into the processor 304 Network.

This processor 304 Network structure allows a wide range of minimumstandards to be set by each government and allows the monitoring of theeffects across the range of minimum standards to determine relative costand benefit of individual standards.

Any domain-specific processor 304 will preferably include a patientsafety comparison processor (PSCP) which compares the MPPC and alloccurrences within the MPPC, such as events, binaries, images, andcascades, to other MPPCs and all of the occurrences of the other MPPCsto identify statistical differences between the MPPC which areassociated with improved or ad verse expenditure, outcome, length ofstay, morbidity, mortality, resource consumption, and/or complicationsto name a few.

One advantage of the processor 304 is that the objects of the MPPC arediscrete and are therefore readily incorporated into statisticalsoftware components of the PSCP. Specifically the processor 304 providesthe ability to positively identify the existence of an occurrence withina patient's set of stream s given a specific occurrence definition andsearch window of time. In the contemplated embodiment, the process ofidentifying any of these occurrences is accomplished through apolymorphic image identification method. The unified method of searchingand/or identification according to this embodiment, allows theincorporation of a range of statistical tools thereby providing accessto the functionality of a wide array of statistical approaches andmethodologies, as are well known in the art, for identifying differencesin discrete time related data collections.

The PSCP provides farther optimization through basic occurrence querycapabilities. For example, a query may be crafted to include a specificoccurrence, the domain (e.g. Local. Hospital. Organizational.International), and a set of conditions. The result set would providepatients along with the time spans in which the specified occurrenceobtained. Aggregation, of result sets provide an indication of thepercentage of identification within the specified population (e.g. 434(0.003%) patients within the specified domain (˜2.3 Million) meeting theconditions (234,046) were identified as having the specifiedoccurrence).

The objects also include organized collections of an ascending hierarchyof complexity and the organized collections, which may be comparedstatistically at each ascending level of complexity to identifyassociated differences. In one embodiment the PSCP divides the MPPCsinto groups having a least a portion of substantially the same images.For example a grouping may be derived having substantially the sameinitial sepsis cascade picture and similar co morbidities and age andsex but different physicians, hospitals and/or treatments. Differencesin length, progression, compilations and mortality associated with thecascade may be identified and statistically compared with thedifferences in physicians, hospitals, treatments, testing, and/ortreatment timing.

In one embodiment, the processor 304 is supported by an OnlineTransaction Process (OLTP) system to optimize data inclusion andrelational management and image identification while the PSCP includesan associated Online Analytical Processing (OLAP) database optimized forthe data mining with regard to occurrence presence and analysis withinlarge populations, occurrence identification within a patient provides aprimary measure while dimensions are established for time, patientcharacteristics, personnel, etc. The multidimensional data environmentallows for the inclusions of dimensions as data is available and/ordetermined as providing important data segmentation. Hierarchies (e.g.domain hierarchies, personnel hierarchies) are included to “roll up”aggregations.

When a particular testing, treatment, bandwidth variation, wardlocation, or hospital location is identified as statistically associatedwith improved outcome, then the International processor 304 may offer,as for download, new protocols which incorporate those identifiedparticulars into the Hospital processor 304 s and/or Organizationprocessor 304 s for their consideration. New medication or treatmentsmay be assessed in this way with blinding accommodated by the processor304 such that the time series of the experimental medication is labeledwith an experimental code.

In one embodiment, the PSCP applies a top down approach to statisticalanalysis of objects wherein an expert or panel of experts at any levelfrom the ward to the international level, defines the statisticalcomparisons, which they desire. This may be performed by a ProbabilityAssessment Studio, for example as by drag and drop of occurrences(selecting relational timing ranges for the objects), such as events,binaries, images, etc. into a first window and then drag and drop ofoccurrences also selecting relational timing ranges for these objectssuch as a blood culture result, a diagnosis, a medication, a combinationof medication and another object, to name a few. The processor may thenprovide the statistical comparison indicating the statisticalrelationship of the compared objects.

In one embodiment, the Probability Assessment Studio indicates NearMisses and their percentages as well as the probability of hits. Thedefinition of a near miss must be specified within the occurrencedefinition. Here it is important to contrast as for example by multipleregression. The processor 304 is applied to allow individual andcombined events, binaries, and images to be compared statistically totargets (which target may be each other). This mitigates the primaryweakness of the application of multiple regressions to definerelationally statistical values as a function of a plurality ofdifferent relational variables. While many reading the medicalliterature are compelled by the assumption laden mathematicalembellishments, which commonly include many multiple regression equationbased evaluations of complex relational physiologic data sets, a trulyskilled mathematician is aware that the application of multipleregression equations to relational physiologic datasets will commonlyproduce outputs of dubious mathematical integrity and reproducibly. Thisis one of the advantages of statistical processing using the processor304 which allows statistical analysis to be applied with a much widerdiscretion over a priori separation and combination of images.

According to one aspect of the present invention a Patient SafetyDiscovery Processor (PSDP) is provided at one of the levels but ideallyat the level of the International Patient safety processor. Like thepatient safety comparison processor, the PSDP compares (as for exampleby statistical analysis) occurrences such as events, binaries, images,cascades, repeating occurrences and patterned occurrences to otherobjects termed “highly definitive objects” such as final diagnoses,results oft“highly definitive” lab tests (such as a blood culture,pregnancy test, or HIV test), length of stay, expense of care, number ofcare workers providing care, number of care workers providing care toothers (workload of the care-workers), consensus based diagnosis, toname a few. However, in contrast to the PSCP, the PSDP applies a “bottomup” approach by performing statistical analysis on occurrences(potentially substantially all objects) in the processor 304 database toidentify previously unknown relationships. These relationships mayinclude statistically significant relationships with each other andespecially with objects including at least one final result, diagnosis,and/or outcome.

In one embodiment The Patient Safety Discovery Processor may determinethe probability that a first occurrence is associated with a highlydefinitive object and then determines the probability that a secondoccurrence which occurs after the first object. The processor thendetermines the probability that each consecutive object is associatedwith the highly definitive object. This generates a time series ofprobabilities with each probability data point being derived with eachnew occurrence type (which may be generated each time a new object isadded inside the occurrence type definition or each time the probabilityof the occurrence of the highly definitive object changes as a functionof a new object which is added to the definition). Here a plurality ofprobability time series may be derived, one or more for each highlydefinitive object. These time series of probabilities may be convertedinto objects and processed to detect and characterize events of risingor falling probability (termed probability events), by theobjectification processor incorporated into the processor 304. In apresently contemplated embodiment, the baseline probability isestablished (pretest probability) based on non-volatile risk factors andthen the occurrence type definitions are built (after the initialprobability has been determined by time insensitive or pretestprobability assessment) so that the slope of the probability time seriesis less dependent of non-volatile factors.

The patterns of the time series of Probabilities (TSP) generated fromsequential occurrences within an MPPC may exhibit characteristics, whichprovide more information then is provided by individual probabilitiesalone. For example, when a rise event (along the time series ofProbabilities) is detected the slope and magnitude of the rise is known.A rise event along a TSP, which is of high magnitude and exhibits arapid slope is said to define “probabilistic momentum” (PPM) which maybe positive or negative. The presence of a high positive PPM providesstrong evidence of the presence or future detection of the highlydefinitive object (or diagnosis) under test. TSP may be generated by theprocessor for each diagnosis for which an increased probability isidentified. Treatment of a pathophysiologic process such as sepsis (ifeffective) will cause the generation of positive reciprocation along theTSP indicating first a positive PPM, followed by a negative PPM. This isa desirable pattern, since if the negative PPM is maintained: thisprovides evidence that the catastrophic event has been aborted. Therapy,which is statistically associated with a positive probabilisticreciprocation along the TSP is identified by the International processor304.

FIG. 25 depicts one embodiment in which the PSDP performs statisticalanalysis to create and/or modify an image definition set. The PSDP,provided with an event definition set 1250 and binary definition set1252 (and optionally an image definition set 1254) as well as a domainof time series data, in association with the processor 304 and PSCP,provides an environment for guided image discovery. The output of thisdiscovery process is a statistically enhanced occurrence definition set1246.

The first step for the researcher using guided image discovery is toidentify a domain of patients (or patient-grouped time series sets) towhich discovery will be applied (The aggregate of 1240 and 1242). Next,the researcher must attach each patient with a Boolean flag indicatingthe presence or absence of the condition or diagnosis to which thediscovered images will be related. In an example the researcher mayidentify a definitive object, which includes a “stand alone diagnosticmeaning” (as determined by a blood culture object or pathology objectfor example). In one embodiment, this definitive object is used to beginthe process of identification. The processor 304 marks all patients thatcontain this definitive object as positive (placing them logicallyand/or physically into the Positive Patient set 1242), but theresearcher may override this designation to weed out subsequentlyidentified false-positives. The PSDP provides tools to make individualpositive or negative designation or designation by the satisfaction of arule-set. In this way, either explicitly, by rule all patients areplaced logically (and/or physically) into one of two patient sets—thePositive Patient set 1240 or the Negative Patient set 1242.

Once all patients in the domain have been designated as positive 1242 ornegative 1240 then the PSDP may begin to build images correlated to thecondition or diagnosis of interest. The PSDP assumes access to theresults of standard processor 304 and PSCP analysis against all patientswithin the positive and negative groups using the supplied event 1250and binary 1252 definition sets. An initial image definition 1254 setmay be supplied as well, but is not required.

The process of building statistically enhanced occurrence definitionsets 1246 depends on the ability to construct occurrence definitionsusing occurrences (found in the positive patient set) and finding thestatistical difference between their existence in the positive 1242 andnegative 1240 patient sets. (As defined above, an occurrence may be anevent, relational binary, image, repeated occurrence or patternoccurrence to name a few). This process is iterative and may begin withbasic occurrences (e.g. events) and then moves to more and more complexoccurrences (e.g. images, repeating occurrences and patternoccurrences). Each occurrence definition created is placed into theCandidate occurrence definition cache 1256.

In an alternative and complimentary embodiment, depicted in FIG. 26, theresearcher may designate reference patients 1260, 1262, 1264 from whichthe candidate images must be derived (rather than from the entirePositive Patient set 1242). Further, the researcher may specify specifictime series segments within the reference patients which the researcherunderstands to be significantly correlated to the condition beingtargeted. Alternatively. or in combination, the researcher may indicatecertain time series segments to not be considered.

For each candidate occurrence definition created the PSCP query engine1244 is used to execute two queries, which are identical except for thedomain (one for the positive 1242 domain, and one for the negativedomain 1240) to determine the percentage of identification. Thesequeries can, as shown above, at the researcher's request (or in a wayautomated by the processor 304) may be limited by any number ofpopulation, risk factor, preexisting condition, or other segmentationdimensions to identify statistically significant subpopulations.

In an example, a primary element in determining correlation may be theDistinct Patient occurrences, which may be defined as the number ofpatients in which the occurrence was identified at least once. Multipleoccurrence identification is communicated to the researcher and may beweighted as an additional factor in the inclusion of an occurrence type.As well, when recurrence of the same or similar occurrence isidentified, the PSDP may attempt to add a repeated or pattern occurrenceto the working occurrence type.

The difference between the percentages returned from the above queriesindicates the statistical significance index (SSI) of the occurrence.The researcher will set a threshold by which the PSDP may determinewhether the occurrence type is retained or discarded. If the occurrencetype is retained it is stored (as depicted for example by 1247) alongwith its SSI 1248 with regard to the specified condition or diagnosis.At the completion of the process a set of occurrence type definitionsare stored and ranked by SSI. The occurrence definition group, alongwith their respective SSI designation, is stored in the statisticallyenhanced occurrence definition set 1246 as related to the specifiedcondition or diagnosis.

In one embodiment, the PSDP begins with basic occurrences: occurrences,for example, including a single perturbation event or single relationalbinary. Once these images have been analyzed the statisticallysignificant occurrence s definitions are stored, and the PSDP begins theprocess of creating more complex occurrence definitions. To accomplishthis process, a plurality (such as three) key time span elements may bedesignated. Each time series involved may be specified with anindication the maximum phase shift of their data. The maximum phaseshift indicates the maximum time delay from the real-time occurrence ofthe data and the recording of that data. Secondly, events and binariesmay be designated with an indication of their maximum span of influence.This maximum span of influence may be defined in terms of real time(e.g. 30 seconds or 2 hours) or may be a relative identification (e.g.patient stay, 3 times event duration). A hierarchy may be provided todetermine the contextual maximum span of influence. For example, a timeseries may be designated to have a maximum span of influence andtherefore, unless otherwise overridden, substantially all events thatoccur along the time series will inherit the maximum span of influencefrom their time series, binaries, which are including events from twotime series, will inherit the greatest of the span of influence of thetwo time series (unless specifically overridden). In some cases, whenfine-tuning is required, the maximum span of influence may be designatedas from one specific time series of the two. Further, a maximum span ofinfluence may be designated within the relationship between two timeseries. In other words, a time series may be known to have a specificspan of influence on another time series. The direction of thisrelationship may be specified as well, since the influence from one toanother time series may not be the same as the influence of the secondtime series to the first. Individual events and binaries may also bedesignated as having a specific maximum span of influence. In oneembodiment, when multiple maximum spans of influence are in effect for abinary or image, the greatest maximum may be applied. Finally a thirdkey time span may be a single tolerance element that the researcher mayindicate to widen the search.

In one embodiment, specifically for the construction of occurrences(e.g. binaries and images) the PSDP uses these three time spans todetermine the scope of search for elements to combine to produce theoccurrence definition. In this embodiment, when the PSDP is looking foran element to combine with a given occurrence, the search looks for theoverlap of the following time spans:(O ^(S) −T) to (O ^(E) +T)(E ^(S) −E ^(MPS)) to (E ^(E) −E ^(MSI))+E ^(MSI)))

Where:

O^(S)=occurrence Start ti me (time)

O^(E)=occurrence End time (time)

T=Tolerance (time Span)

E^(S)=Element Start time (time)

E^(E)=Element End time (time)

E^(MPS)=Element Maximum Phase Shift (time Span)

E^(MSI)=Element Maximum Span of Influence (time Span)

The PSDP uses this scope of search to identify all occurrences thatshould be combined with a working occurrence to determine if adding thenew occurrence to the definition results in a statistically significantoccurrence. In this example, the search is conducted within a singlepatient and only occurrences that exist within the positive patientdomain are used. For efficiency, the researcher may further indicatethat the occurrence must be found at least X number of times within thepositive patient domain before creating a new occurrence definition totest against the negative domain. The analysis of the timingrelationships among multiple variations of the image within the positivepatient domain provides time interval data for determining the searchwindows within the occurrence definition itself, and an additionaltolerance may be configured here as well. Once a new element has beenchosen as providing the core for building a new occurrence definition,the occurrence definition is then used to query for SSI and to determinewhether it meets threshold SSI so as to be part of the statisticallyenhanced occurrence definition set. Regardless of its inclusion in thestatistically enhanced occurrence definition set, the new occurrencedefinition is stored to build additional combinations since thestatistical significance of component occurrences is not alwayscorrelated with the statistical significance of a complete occurrence.In fact, a given occurrence component may predict the existence of aspecific definitive object with modest probability, whereas once thecomplete occurrence definition has been built, the presence of thecomplete occurrence definition may actually predict that the definitiveobject will not occur and vice-versa. In this way, the PSDP continues tobuild more and more complex occurrence definitions to be analyzedstatistically until no more combinations within the scope of search areidentified.

To support the incremental creation of occurrence definitions, theprocessor 304 supports query capabilities to return potentiallysignificant occurrences. This query returns a result set of componentsas follows: Given (occurrence definition to be Enhanced. PositiveDomain); Return (Element) Where (Element is in Scope of Search) Furtheran aggregation query may be submitted as follows: Given (occurrencedefinition to be Enhanced and Positive Domain) Return (Element type.Count of occurrences Found, Count of Distinct Patient occurrences Found)Where (Element is in Scope of Search) Order By (Count of DistinctPatient occurrence)

During this analysis, the PSDP further maintains the definitions ofantecedent relationships between occurrences identified. An evaluationof these antecedent relationships provides a further element in thestatistical analysis. The PSDP may identify the most predictive (e.g.most statistically significant) paths of evolution of images along thetimeline of the MPPC. This information will help refine protocols to betargeted not only images but image paths. The image (or otheroccurrence) with the maximum SSI may be designated as the primary image.Alternatively, multiple images (or occurrences) may be designated asprimary the fact that they are the maximum SSI within their associatedevolutionary path.

The PSDP may assist in the location of binaries, but more readilyassists in the detection of certain binary types. For example, Expectedand Analogous binaries may be identified with the PSDP, but VerifyNon-Existence binaries require much more processor power since the PSDPuses non-existence of an event as a criteria for search, which greatlyextends the scope of the search. In another instance the existence ofevents, which are similarly or identically, predictive may suggest thedefinition of an analogous binary, an analogous image, and an indicationof potentially redundant or superfluous testing and the PSDP may providethis indication to the researcher.

In some cases, if a binary is identified as not predictive, an event (orboth events) within that binary may be identified as predictive. Here itmay be seen that one or more relationships between events may be morepredictive then the presence of the events. This is one of the reasonsthat conventional statistical regression as applied to even a very widerange of detected events (without building the relational binaries andmore complex occurrences (e.g. images) and more specifically withoutconsidering the complex relational patterns and the timing of thepatterns of the events in relation to each other) is incomplete andpotentially misleading.

Because the PSDP is very sensitive to event segmentation, the researchermay instruct the PSDP to break individual event definitions into acontinuum of event sub types (as, for example, pertaining to slope,magnitude, threshold breach, components of the definition, to name afew). The PSDP will search both on the original event type and on eachof the event subtypes to look for a threshold breaking SSI. Once thisprocess is completed and the statistically enhanced occurrencedefinition set is available for use in the processor 304, the processor304 may search for all occurrences within the statistically enhancedoccurrence definition set. Once all occurrences have been identified,the processor 304 may obtain the Maximum SSI (MSSI) among all of theidentified occurrences. Further, the MSSI for specific conditions anddiagnoses under test then may be added as additional parallel timeseries and available for analysis by the processor 304 and visualizationin the patient safety visualization processor, events within these MSSItime series are of particular significance. An up event in an MSSIindicates the movement of the system toward a positive predictiveidentification of a condition or diagnosis. From the slope of a timeseries of statistical values and the instantaneous statistical value ofthat event is derived Probabilistic Momentum within the processor 304and patient safety visualization processor. According to an embodimentof the present invention, a high product of the positive slope, whereinthe outputs of the testing used to derive the slope are time sensitive(i.e. the outputs change with time as a function of the presence orabsence of the disease state or the presence of the definitive object),provides additional predictive indication beyond the that of theinstantaneous predictive indication. The presence of high probabilisticmomentum is highly predictive and may also provide an indicate acuitywith certain disease states. The patient safety console may beconfigured to show the parallel time series of MSSI for the top Xdiagnoses or may be configured to show the MMSI with the greateststatistical momentum. Parallel probability time series may be generatedby the processor for each diagnosis for which an increased or reducedprobability is identified. Treatment induced reversal of apathophysiologic process such as sepsis will cause the generation ofpositive reciprocation along the time series of probabilities forsepsis.

With retrospective analysis, Probabilistic Momentum toward PrimaryDiagnosis is a Key Performance Indicator KPI). The increase of this KPImay be used to determine the effectiveness of a diagnostic environment.Elements (e.g. diagnostic tests) will be able to establish efficacy bydemonstrating the increase Statistical Momentum toward Diagnosis. WithProbabilistic Momentum toward Diagnosis and Cost added as measures inthe PSCP multidimensional database the PSCP will be able to demonstratethe cost-effectiveness of diagnostic tools. In one embodiment, the PSDPutilizes the process of Retrospective Real-time analysis (RRA) tofurther investigate probabilistic momentum. RRA uses a time-slicingtechnique to examine a set of time series, and the associated objectstream s, in the way they would have presented in real time. Toaccomplish this, the processor 304 creates a set of MPPCs, each of whichrepresents what the MPPC would have been at a specific point in time. Inother words, the processor 304 selects a point in time and truncates theMPPC to only include the data that existed at that point in time andbefore. In this way, and in combination with the true final MPPC,partial occurrences may be analyzed to investigate their evolution andthe definitions that may be employed to identify them. The PSDP maycompare a partial occurrence with the final occurrence to look forindicative elements. As well, the PSDP may compare partial occurrencesacross a wide range of patients and conditions. For example, the PSDPmay determine partial occurrences that are very similar and that willcomplicate early detection and look for key elements that differentiatethem.

In this manner future binaries, images, and cascades are constructedfrom the bottom up as more and more patient data sets are available. Inessence, the entire pool of patients monitored by the processor 304,include experimental datasets whereby positive or negative statisticalassociations may be determined automatically and new relationships,disorders, predictive sets of testing, and superfluous testing,effective treatment, superfluous treatment, ineffective treatment andharmful treatment may all be identified from the bottom up as a functionof statistical relationships.

The application of Probabilistic Momentum evaluation exploits one of themost important features of living organisms: the time dependency ofpathophysiologic processes and of physiologic relationships. Aspreviously described, perturbations and the pathologic or physiologicresponses of organisms to perturbations are relationally time dependent.Each living organism is a specifically structured chemical, electrical,and mechanical entity. The relational structure of each of thesecomponents of the organism defines the timing for chemical, electrical,and mechanical action. Therefore actions, reactions, and failures ofthis structure will occur with order within definable limits as afunction of the relational structure, which will define the temporalrelationship limits of actions, reactions, and failures.

In the presence of a pathophysiologic cascade, increased probabilisticmomentum may be induced by a more rapidly progressive pathophysiologiccascade but may also be induced by more rapid data sampling, broaderbandwidth of testing (more testing per unit of time), and/or betterfocused testing. Probabilistic momentum is therefore both a function ofthe rate and magnitude of pathophysiologic cascade progression as wellas the quality and scope of the testing applied to render the paralleltime series. When the testing and expansion of the bandwidth is thesame, a higher degree of probabilistic momentum suggests a greater rateof pathophysiologic cascade progression.

In one embodiment, a system and method according to the presentinvention is provided wherein, events, binaries, and images are definedby time aggregations wherein event aggregations are primarily (orsolely) based on time (as by windowing for example). This exploits thetime dependency of physiologic systems and of pathophysiologic processesand is simpler than the rendering of binaries images, and/or cascades aspreviously discussed. Such time aggregations may be used to complementthe more complex building of the motion pictures of physiologic failure.In addition binaries, images, and/or cascades may be derived from a timebased aggregation (as by combining all permutations (or specific sets ofpermutations) of the event objects. Alternatively, or in combination,events binaries, and images may be additionally or similarly aggregatedby magnitude, slope, and/or pattern and/or type or other characteristic.

According to another aspect of the invention each time relationship maybe converted to an object such that at east one portion of an event,binary, image, and/or cascade is comparable with another portion of anevent, binary, image, and/or cascade. In an example, the timerelationship of at least one portion of alpha event is compared to atleast one portion of the beta event to derive at least one intra binarytemporal relationship (which may be an object or a characteristic of thebinary object). The intra binary temporal relationships (or anothertemporal relationship) include values, which may be converted to a timeseries, objectified and incorporated into the MPPC as desired. Similarlythis may be performed for images and cascades. In an example,acceleration of the cascade may thereby be readily identified.

In one embodiment the processor is programmed to detect a sentinel eventobject (such as a rise in WBC count, a rise in respiration rate, or arise in temperature). The processor then identifies all event objectsoccurring within a retrospective and prospective time window of thedetection of the sentinel event. In an example, upon the detection of arise in temperature the processor may aggregate all event objects, whichoccur within 48 hours after the temperature is elevated, and thisaggregation may be compared to aggregations (built and/or derived fromstatistically derived aggregations in relation to definitive objects.)The primary relationship rendering the aggregation of these eventobjects is time but the event objects within the aggregation may becomprehensively compared.

With a time aggregation, upon the addition of each new event, theprobability of the future occurrence of a plurality of definitive eventsmay be determined. As discussed, Probabilistic momentum is derived whena plurality of time sensitive (volatile) events, each adding greaterprobability is detected and the time series of the probability of aplurality of definitive events may be plotted, objectified and analyzed.

In another embodiment, the processor repeatedly cycles (which may be acycle of windowing) through the time series looking for (for example)objects (such as perturbation events). Each time an object is identifiedit is placed (for example) in a perturbation event set if this is theobject type being detected. Consecutive sets are derived for each cycle.The set derived from each cycle may include no perturbation events orhundreds of perturbation events depending on the state of the organism.This approach essentially aggregates objects (events) by cycles (thecycling frequency may range for example from about 0.01 second to about1 hour depending on the definitive object). Consecutive sets ofperturbation events include objects and therefore have thecharacteristics of the objects, as well as the number of the objects,and the probabilities associated with the objects. These characteristicsmay be used to derive various time series, which may be objectified andanalyzed. In an example, if along a time series of perturbation events,the number (and/or magnitude) of perturbation events shows a highpositive slope, but none of the time series of the probability for anydefinitive process shows a high positive slope, this suggests that theprocessor is failing to identify (and may not timely identify) the causeof the perturbation and this may provide a warning that intensive expertphysician diagnostic evaluation is timely required. The processor 304 inthis case is saying, the patient's physiologic system is progressivelymore perturbed, and I am not identifying a likely cause at a ratematching the growth of the perturbation and the patient needs experthuman brain help soon.

Perturbation object sets may be derived globally (from all availabletime series) or may be derived from a focused set of a particular groupof time series (such as time series which relate to inflammation forexample). The relative pattern of growth or decline of each differentset may be compared by objectifying selected time series ofcharacteristics of the sets. In an example, during sepsis, a set ofinflammation perturbation objects may grow rapidly (with an increasingnumber and/or magnitude of perturbation objects and or decreasing timeinterval between increases in number and/or magnitude), and then thisgrowth may be followed a rapid growth of a hemodynamic perturbationobject set. The comparison of objectified time series of the setsrenders parallel rise objects producing a binary with the inflammationset rise including the alpha event and the hemodynamic set riseincluding the beta. Upon detection, the occurrence of a respiratory setrise may then be added to produce an image (derived of time series ofcharacteristics of sets) and finally with additional sets a motionpicture of physiologic failure derived of time series of characteristicsof sets (wherein the characteristics may for example include number ofperturbations, magnitude of perturbations, frequency of perturbations,slope of perturbations, probability of a given definitive object to namea few).

One embodiment provides an example of the continuous derivation ofspecific objects (such as events, binaries and images) between two ormore time series to both corroborate the significance of an object aswell as to exclude perturbation due to artifact. In an example, it iswell known that motion may cause artifactual desaturation events alongan SPO₂ time series. However, motion may also occur in response to adesaturation event (particularly in response to the arousal from thedesaturation). The proximate existence of arousal motion corroboratesthe SPO₂ event, binaries and images and helps to establish that thepatient was sleeping or sedated at the time of the desaturation. Motioninducing artifactual desaturation and arousal motion responding to atrue desaturation are generally different in relational timing,relational spatial pattern, and the relational frequency pattern andcharacteristics. After adjusting for any phase shift, a arousal motionobject inducing artifactual desaturation object will generally earlyoccur at the same time or precede the onset of a desaturation object.Whereas arousal motion object will occur after the onset of thedesaturation object or adjacent the recovery from the desaturationobject. In addition after adjusting for any phase shift, arousal motionobjects commonly occur in a specific pattern, which mirrors and/orcorresponds, in at least one aspect, with the pattern of SPO₂ objects,with the pattern of desaturation objects within the pattern of the SPO₂objects preceding the pattern of motion objects. Furthermore motionarousals within a cluster are usually brief (about 2-6 seconds) and thenfollowed by a period of little or no motion. In one embodiment the highfrequency components of the frequency spectrum of the plethysmographicpulse waveform is used to indicate motion or another known method isused to either indicate threshold motion as a step function time series(as is for example outputted by the Minolta 300 i pulse oximeter) orpreferably a graded motion time series between the values of 1-10 isprovided.

The following provides an example of an occurrence of an image of“Hypoxemia Induced Micro-arousal” (HIM), which is commonly indicative ofsleep apnea induced hypoxemia. In one embodiment the HIM image isderived from a motion time series (as generated using detection ofmotion from the plethysmographic time series or by the use of atigraphyor another method), a SPO₂ time series, and a pulse time series (such asa plethysmographic pulse time series). All of these time series may begenerated by a patient mounted pulse oximeter. The processor 304 detectsa SPO₂ event, binary, or image within the span of influence of themotion event, binary, or image and the pulse event, binary, or imagewithin the span of influence of the SPO₂ event, binary, or image.Together, the onset of a fall in oxygen saturation followed by the onsetof a brief episode of motion (e.g. 1-6 seconds), a rise in heart rate(and/or another property of the pulse time series) and a rise in SPO₂produces an occurrence of an image of HIM. In some cases the motionevent, binary, or image may be indicative of pathology as a function oftheir characteristics alone. In an example the detection of motionoccurring in a periodic cluster pattern typical of a cluster of HIMinduced by a cluster of apneas as defined for example byobjectification, FFT processing, by a combination of frequency domainand spatial domain processing, or another method) whereas at other timesthe decision to reject a given SPO₂ event, binary, or image may be madewith consideration of the relational timing and relational pattern ofthe motion and the other signals. In an example the presence of acluster of motion reciprocations in relational combination with acluster of SPO₂ reciprocations provides stronger evidence of thepresence of a cluster of HIM. This is further supported by the detectionof a prolonged period of motion (for example 15 seconds or more at theend of a SPO₂ reciprocation cluster (of high SPO₂ desaturationmagnitude). This terminal motion is typical of a hypoxia-inducedawakening (HIA) induced by the cumulative effect of a cluster of severeapneas. This is dangerous image to identify in a patient being treatedwith patient controlled analgesia because the awakening may trigger theperception of pain causing the patient self medicate at a vulnerabletime. The processor 304 may therefore be programmed to detect thecombination of image of HIA in combination with PCA treatment and tosend an indication to the PCA device to prevent a patient bolus from thePCA device upon the detection of HIA and during an interval (for example30 minutes) after any image of HIA has been detected. The detection ofHIA in the presence of PCA if followed closely by a self-administeredbolus would provide strong evidence of overmedication (or undertreatment of the sleep apnea).

One method for inclusion of a SPO₂ pattern as non-artifactual includescomparing the SPO₂ time series to a time series indicative of motion anddetermining the SPO₂ time series as non-artifactual based on both thetiming and characteristics (which may be a spatial and/or frequencycharacteristic) of perturbations along the SPO₂ time series to thetiming and characteristics (which may be a spatial and/or frequencycharacteristic) of perturbations along the motion time series. In oneembodiment this may include identifying a desaturation as nonartifactual by identifying that at least 4% of the desaturation occurredbefore the onset of threshold or trend of perturbation along the motiontime series or that at least ⅓ of the desaturation magnitude occurredduring a period without motion. Another method for inclusion of acluster of desaturations as non-artifactual includes identifying acluster pattern of desaturations, identifying a cluster pattern of briefmotion events wherein the cluster patterns correspond in frequency orwherein the motion events correspond to a greater extent to the recoverythan to the desaturation. Another method for identifying a desaturationor cluster as non artifactual includes transforming the plethysmographicwaveform into frequency components (as for example by FFT), identifyingthe desaturation or cluster as non artifactual if the frequency spectrumshows a higher degree of high frequency components (or a broaderbandwidth) during the recoveries from the desaturation than during thefirst portion of the desaturations.

In another example time series data sets of the slope of the recoveries,the magnitude of recoveries, and the recovery magnitude ratio (magnituderise/magnitude fall of each reciprocation), duration ratio (durationrise/duration fall), area ratio (area above or below the curve rise/areaabove or below the curve fall), of each reciprocation) of the SPO₂ isgenerated and compared to a time series of narcotic infusion. Narcoticsmay both diminish the slope and magnitude of the recovery and mayincrease the magnitude of the fall and may reduce the recovery ratio.Therefore according to one aspect of the present invention aperturbation pattern or threshold change in one of these values (such asa 50% fall in the mean slope of the recoveries for 10 minutes may beused to trigger an indication of possible excessive narcotic treatmentor to lock out the PCA.

These provide examples of relational images at the event, binary, andimage level, along with specific properties and relationships which aredefined in each micro domain and added to the processor 304. In thisexample the images of sleep apnea encapsulates the images of HIM, HIA,and motion-induced artifact, which represent a few of the basic imagesof the comprehensive processor 304.

In an example, the processor 304 may generate and output a cyclingseverity index (CSI) for the entire night) by the formula 1 and 2:Ts=Tt−Tm  1.Where:

-   -   Ts=study time    -   Tm=motion time (other than micro arousal motion)    -   Tt=total time        CSI=[(Tc/Ts)/(Sum Tr)/Tc _(j)]mean delta SPO₂  2.        Where:    -   Tc=cycling (cluster) time for the entire night    -   Tr=recovery time (between cycles within a cluster of cycles)    -   Ts=the study time with or without the rejected artifact time        Instead of the mean delta SPO₂ the greatest 10% or the greatest        10 minutes or other portion or quantification of the delta SPO₂        may be used.        In addition, real time (windowed) CSI (WCSI) may be given by        formula 3:        WCSI=[(Tcw/Tw)/(Sum Tr)/Tc]mean delta SPO₂  3.        Where    -   Tcw=cycling (cluster) time for the calculation window (e.g. 5        minutes)    -   Tw=total window time (e.g. 10-15 minutes)        Alternatively CSI may be adjusted for recovery failure to        produce an RCSI with the formula 4.        CSI(baseline SPO₂−mean recovery SPO₂)  4.        Where:    -   all negative values for the difference are rendered equal to 1.        In the calculation of the recovery CSI, rather than the baseline        SPO₂ a value such as 90-93 may be used or the recovery can be        mathematically compared to the peak values before the falls.        Further, instead of the mean recovery SPO₂ the lowest 10% or the        lowest 10 minutes or other portion or quantification of the        relative or absolute magnitude and/or slope or the recovery may        be used.

In one embodiment the objects derived from narcotic and or sedativeinfusion is combined with the objects of recovery to produce an imageindicative of drug associated recovery failure wherein for example, theimage contains an occurrence of drug infusion followed by an occurrenceof a fall in recovery peak values and/or a fall in recovery slopevalues. The presented embodiments represent a few examples of patientsafety processing technology applying the objectified time-seriesmatrix. As noted earlier when a new micro domain is added to the matrixthe question may be asked, given the addition of the new occurrenceswithin this micro domain, does any new image within the matrix have agreater probability of being associated with a definitive object thenwithout the addition. Many new significant relationships and measureswill be identified using this method or by automating the process toprovide adaptive processor 304 processing wherein the processor 304builds and cycles though a broad range of micro domains looking foroccurrences which, when added to the matrix change the probability in asignificantly positive or negative way.

FIG. 24 is a data flow diagram of the patient safety processor network.Two active medical facilities (1200 and 1207) are connected to acentralized image data center 1210. In one embodiment, facilities wouldinclude, at minimum, two processors: the Patient safety processor 1201,and the patient safety visualization processor 1202. As well, eachfacility would include, at minimum, two databases: the patient safetyimage database 1204 containing aggregated patient data with theassociated objectified time-series matrices, and the Safety imagedefinition Database 1205 containing all of the occurrence definitions(event definitions, binary definitions, and image definitions to name afew) required to construct the Objectified time-series matrix from rawphysiological signals and other inputs described above.

Patient monitoring data, including the constructed Objectifiedtime-series Matrices flow this direction into the Aggregate patientsafety image database 1212 increasing the available data against whichresearch and other reporting activities may be conducted. This data flowmay be facilitated by database synchronization, message queuing, EDI, anEnterprise Service Bus, Asynchronous Web Services. Business ProcessManagement software or other enterprise orchestration servers to name afew. In an example of a embodiment, the Centralized image Data center1210 contains three databases: the Aggregate patient safety imagedatabase 1212, the safety image definitions Database 1216 and thePatient Safety Data Warehouse 1214.

The Aggregate patient safety image database 1212 contains the aggregateddata from the Patient Safety image databases at any number of medicalfacilities. The Patient Safety Data Warehouse 1214 is a derived databasegenerated by analysis of the Aggregate patient safety image database andis an Online Analytical Processing (OLAP) database optimized for thedata mining with regard to occurrence presence and analysis within largepopulations.

In an example of a embodiment, the safety image definitions Database1216 is a centralized repository of occurrence definitions (for exampleimage definitions) that research has determined to be most accurate withrespect to probabilistic momentum. These may be accessed, as describedabove, by any number of facilities to enhance their ability to identifyand treat conditions, occurrence definitions added, changed or deletedfrom the Safety image definition Database 1216 may be distributed to themedical facilities by individuals or committees specializing in thisdistribution or by predefined transmission protocols.

The Research Facility 1220 depicted in FIG. 14 is a set of software thatsupports a centralized research team in creating, testing, updating anddisseminating processor 304 metadata in the form of occurrencedefinition sets. In an example of a embodiment, the Research Facility1220 contains the patient safety comparison processor (PSCP) 1222, thePatient Safety Discovery Processor 1224, the Probability AssessmentStudio 1226 and the Guided image Discovery 1228 software. These softwarecomponents, all described in detail above, facilitate discovery,verification, refinement, probability assessment and storage ofoccurrence definition sets.

FIG. 28 is a user interface model of the occurrence definition editorwhich, in an example of a embodiment, is a software tool used tovisually construct and persist the occurrence definition set. Theoccurrence definition editor 1320 is a flexible environment for theinvestigation, identification and definition of micro-domains within aseries of point and occurrence stream s. The environment may beconfigured to allow the researcher to locate micro-domains, to focus onparticular elements within micro-domains or to create properties of aselected micro-domain. As configured in FIG. 28, the occurrencedefinition editor has four distinct sections.

The first section is the Selection Bar 1324 at the top of the screen. Asconfigured this section includes the ability to select and the displayof the occurrence type 1328 and Selected occurrence 1330. The selectedoccurrence type specifies the occurrence definition that the researcheris viewing and/or updating. This occurrence type may be one of thesimpler types (e ent or binary) or may be a more complex type (image,repeating image or Pattern image). The occurrence type may be acandidate type which has not been persisted into the occurrencedefinition set. The occurrence editor also allows for the Researcher tocreate Derivative types in which he/she begins with the definition of adifferent type and makes changes to create a new type.

The Selected occurrence 1330 allows the researcher to choose anoccurrence within an available occurrence stream to be a reference ashe/she works on the definition of the occurrence micro-domain. Once aSelected occurrence is chosen, it may be displayed (with all or some ofits constituent parts) in the bottom (fourth) section of the screen.This allows the researcher to immediately see the results of changes inthe definition within a reference case. The Researcher may switchbetween several reference cases during the course of editing. The secondsection of the screen is made up of several subsections: The occurrencetype Explorer 1332, the Characteristics Box 1334, the OtherParticipation List 1336 and the Dependency Viewer 1338. The occurrencetype Explorer is the primary element of this section and all of theother subsections relate to it and to the occurrence type that isselected within it (e.g. oximetry reciprocation in FIG. 28). Thissection provides the ability for the researcher to search for additionaloccurrence types to add to the definition that is being constructed. Theoccurrence type Explorer 1332 lists all of the occurrence typesavailable to the Researcher to add to the Working occurrence definition.The researcher may drag an occurrence type from the occurrence Explorer1332 onto the Construction Surface 1344 to add an occurrence type to theWorking occurrence definition. A search capability is provided to filterthe list. Once the Researcher selects an occurrence type in theoccurrence editor, the other three subsections within this sectionchange to reflect information specific to the selected occurrence type.

The other three subsections provide information as follows: TheCharacteristics Box 1334 provides a list of characteristics of theoccurrence type selected in the occurrence Explorer. Thesecharacteristics include the Name, type (i.e. is it an event, binary orimage to name a few), visibility (i.e. what domain does this apply to)to name a few. This section may contain workflow details as well toallow researchers to understand the state of the occurrence definition(e.g. what researcher created it, when it was created, whether it isapproved or under review etc.) The Other Participation List 1336displays a list of occurrences in which the occurrence type selected inthe occurrence Explorer is a participant. For example, as shown in FIG.28, the oximetry reciprocation occurrence is selected in the occurrenceExplorer and therefore the oximetry cluster repeating occurrence isdisplayed in the Other Participation List 1336. This is displayed herebecause the oximetry reciprocation is part of the oximetry clusterrepeating occurrence. Any number of occurrence types may be listed here.The Dependency Viewer 1338 shows the dependency model of the occurrencetype currently selected in the occurrence Explorer. The dependencies maybe shown in various views: Dependency Tree Diagram, occurrence List, toname a few. FIG. 28 shows this section showing the dependency diagramfor the oximetry reciprocation. The Dependency Viewer allows theresearcher to examine all of the constituent parts of the occurrencetype selected in the occurrence Explorer down to the raw signalsrequired for the occurrence type to be constructed.

The third section of the screen is the occurrence definition Diagram1340 where the researcher draws (e.g. through drag-and-drop) the diagramthat represents the scope of the occurrence. This section may beconfigured using the Phase Dropdown 1341 to specify the Phase (e.g.Scope Identification. Element Construction, Scope Refinement to name afew) to which the diagram applies. The View Dependencies button 1342allows the researcher to see the dependencies on which the displayeddiagram depends. The diagram itself is displayed on the ConstructionSurface 1344. (The diagram shown in FIG. 28 is explained in more detailin FIG. 10.)

The fourth and final section (as configured) is the time series Section.This section contains occurrence stream s 1346 and Associated pointstreams 1346 (e.g. signals). These streams are added either when aspecific occurrence is selected (Using the Selected occurrencefunctionality 1330) or individually through the Add stream button 1348.The stream s are shown parallel in time. If an occurrence stream isadded, the occurrence definition editor may ask if associated occurrencestreams and/or point streams should be added.

This section allows the researcher to examine real data whileconstructing the micro-domain that they are working on. Changes to theoccurrence definition may be immediately reflected in the time seriessection so that the researcher may tune the definitions to referencepatients. If multiple patients (or patient stays) are required the timeseries section may be split into multiple sub-sections to allow forgroups of stream s to be coordinated in time. In this way, theresearcher may want to view a positive case and a negative case (perhapswith similar, but misleading patterns) so as to refine the occurrencedefinition with precision.

The occurrence definition editor is built to be one part of a rich setof tools that the researcher may use to refine occurrence definitions.For example, the occurrence definition editor links to the PatientSafety Comparison processor to run statistical analysis (for example todetermine SSI) on reference patient sets at the researcher's request. Inthis way, the researcher may immediately assess the statistical impactof changes to the definition.

Further, the occurrence definition editor may be employed along with theimage construction processor during guided image discovery. In this way,the automation (e.g. the location of images that may be statisticallysignificant) provided by the processor may be directed and refined bythe researcher

FIG. 29 show an second exemplary depiction of the occurrence definitioneditor. In this case the occurrence definition editor is configuredslightly different and shows the editing of the Heparin-InducedHemorrhage image. For an overview of the occurrence definition editoruser interface see FIG. 28. In FIG. 29 the Construction Surface is splitinto two sections—Sequenced 1350 and Non-Sequenced 1352. (Thedifferentiation between Sequenced and Non-Sequenced elements of an imageare explained in FIG. 8c ). The bottom section of the occurrencedefinition editor is configured differently than the one in FIG. 28. Inthis case, rather than viewing associated time series, the research rhas selected to see the Qualification Rules 1354 on the bottom left andthe properties/Synonyms 1358 on the bottom right. These sections allowthe researcher to further refine the occurrence definition. TheQualification Rules section 1354 lists the rules that must be satisfiedto qualify the occurrence as a true occurrence of the Target occurrencetype. These rules, as described above, may be Boolean expressions orsets of Preservation Rules to name a few. The rules are listed in thisscreen in a summary way (or by name) but the Researcher may edit them orget a more complete view using the View Rule editor button 1356,properties and Synonyms are listed in the property/Synonym List 1358.(properties and Synonyms are described in detail in FIG. 5 and FIG. 7a.) The properties and/or Synonyms are listed in this screen in a summaryway (or by name) but the Researcher may edit them or get a more completeview using the View property editor button 1356.

The property editor is described in detail in FIG. 30. If a Selectedoccurrence has been chosen then the evaluation result of eachQualification Rule, property and Synonym (if valid) will be displayed.Changes to the occurrence definition Diagram may affect theQualification Rules, properties and/or Synonyms. If changes to thediagram invalidate these elements (for example, removing constituentparts that are part of a property dependency) the editor will indicatethe fact with color, icons, message or all of the above (as configuredby the user). Dependencies, and other characteristics, of QualificationRules, properties and Synonyms may be viewed and/or edited within theassociated editor (either Rule editor or property editor).

FIG. 30 is a user interface model of the occurrence property editorwithin the occurrence definition editor (described in FIG. 28 and FIG.29) which, in an example of a embodiment, is a software tool used tovisually construct and persist the occurrence properties within theoccurrence definition set. The occurrence property editor may be invokedwithin the occurrence definition editor or may be used as a standalonetool.

As configured in FIG. 30, the occurrence property editor has fourdistinct sections. The first section is the Selection Bar 1370 at thetop of the screen. As configured this section includes the ability toselect and the display of the Scope 1372 and Selected occurrence 1374.The selected Scope 1372 specifies the occurrence definition that theresearcher is viewing and/or updating. This occurrence type may be oneof the simpler types (event or binary) or may be a more complex type(image, repeating image or Pattern image). In one embodiment, the Scope1372 may include sub-elements of an occurrence type (e.g. Inflectionpoints). If the occurrence property editor was invoked from theoccurrence definition editor then the Scope 1372 is initially set as theoccurrence type which the researcher was currently editing. The Selectedoccurrence 1374 allows the researcher to choose an occurrence within anavailable occurrence stream to be a reference as he/she works of thedefinition of the occurrence micro-domain. Once a Selected occurrence ischosen, it may be displayed (with all or some of its constituent parts)in the bottom (fourth) section of the screen. This allows the researcherto immediately see the results of changes in the definition within areference case. The Researcher may switch between several referencecases during the course of editing.

The second section of the screen is made up of several subsections: Theoccurrence model View 1376, the (Characteristics Box 1380, and theDependency Viewer 1382. The occurrence model View is the primary elementof this section and all of the other subsections relate to it and to theitem that is selected within it (e.g. instability index Calculatedproperty in FIG. 30). This section provides the ability for theresearcher to search for items (properties, Synonyms, to name a few)within the occurrence model of the Scope to add as elements within theproperty definition that is being constructed. The occurrence model 1384contains the hierarchy of all of the occurrence types within the Scopeand all of their related properties, Synonyms and Rules. The researchermay drag an item (e.g. a property) from the occurrence model 1384 ontothe Expression Construction Surface 1394 to add as an element in theWorking property definition. A search capability is provided to filterthe hierarchy. Once the Researcher selects an item in the occurrencemodel 1384, the other three subsections within this section change toreflect information specific to the selected item. The other threesubsections provide information as follows:

The Characteristics Box 1380 provides a list of characteristics of theitem selected in the occurrence model 1384. These characteristicsinclude the Name, type (e.g. Calculated property as shown in FIG. 30),value (if a Selected occurrence is available) to name a few. Thissection may contain workflow details as well to allow researchers tounderstand the state of the occurrence definition (e.g. what researchercreated it, when it was created, whether it is approved or under reviewetc.) The Dependency Viewer 1382 shows the dependency model of the itemcurrently selected in the occurrence model. The dependencies may beshown in various views: Dependency Tree Diagram, occurrence List, toname a few. FIG. 30 shows this section showing the dependency diagramfor the instability index Calculated property. The Dependency Viewerallows the researcher to examine all of the constituent parts of theitem selected in the occurrence Explorer down to the raw signalsrequired for the item to he constructed and/or evaluated.

The third section of the screen, in the depicted configuration, is madeup of three subsections: the property Expression Box 1340, theEvaluation Result Pane 1392 and a second Dependency Viewer 1396connected to the Working property definition. The property ExpressionBox 1340 and the contained property Construction Surface 1394 providesan editing space where the researcher creates the expression (eitherdirectly or through drag-and-drop) that defines the Working property.The type of the property may be selected from the type Dropdown 1388(for example Calculated property and attribute to name a few). When thetype of the property is changed, the Construction Surface 1394 isreconfigured to the property type. For example, if an attribute isselected then the Construction Surface is broken up into two parts: theBoolean condition and the Value Expression. The Dependencies View 1396allows the researcher to see the dependencies on which the Workingproperty depends.

The fourth and final section (as configured) is the time series Section.This section contains occurrence stream s and Associated point streams(e.g. signals) as in FIG. 28. This section allows the researcher toexamine real data while constructing the property they are working on.Changes to the property definition may be immediately reflected in thetime series section so that the researcher may tune the definitions toreference patients.

FIG. 31 is a user interface model of the occurrence property editor (thesame as in FIG. 30) used with a selected reference patient andoccurrence. FIG. 32 is a sample dependency diagram, specifically andimage dependency diagram depicting the dependencies of a Heparin-InducedHemorrhage image, dependency diagrams provide a visual view ofdependency from the Root Element 1430 down to the Raw point streams(1450, 1452, 1454, 1456, 1458, and 1460).

In an example of a embodiment, a dependency diagram contains a singleRoot Element 1420 which is an icon representing the element which isbeing evaluated as regards dependency. In some cases, the Root Element1420 may be omitted (for example, it is clearly implied within asoftware user interface). From the root element lines stretch downwardto show dependency. In an alternative embodiment (not shown in FIG. 32)the lines may project in any direction but have an arrowhead to show thedirection of dependency. Icons represent the elements of the dependency.Icons may represent occurrence types (as, for example in FIG. 32 thetriangle icon 1438 represents a Heparin Rise event), point streams (as,for example in FIG. 32 the wave icon 1456 represents the Hemoglobinsignal), properties. Rules, Sub-Elements to name a few. Each node withinthe diagram is, in itself, a dependency diagram. In an interactiveenvironment nodes may be collapsed or expanded. Point streams areconsidered Leaf Nodes and have no dependencies. Other Leaf Nodes mayexist. For example, if the dependency for a property is being displayedand that property is a Calculated property with the expression:Slope*0.8 then the dependency diagram would have two top-level nodes—TheSlope property and the number 0.8. In this case, the number 0.8 would bea Leaf Node. Dependency diagrams may be used to show the scopedependency (as in FIG. 32) or a more detailed dependency includingproperties. Rules and Synonyms. This second configuration is most oftenused in showing the dependencies of single values, whereas the firstconfiguration is used most often when focusing on the scope of anoccurrence. The dependency diagram in FIG. 32 is symmetrical but thisneed not be (and often is not) the case.

A dependency diagram may be collapsed into a dependency list. Adependency list simply lists all of the dependencies, but does not showthe hierarchical relationship. The diagram in FIG. 32 may be collapsedto the list:

-   -   Pulse Response in relation to Heparin or adverse PTT pattern        (binary)    -   Hemoglobin Response in relation to Heparin or adverse PTT        pattern (binary)    -   Blood Pressure Response in relation to Heparin or adverse PTT        pattern (binary)    -   PTT Rise (event)    -   Pulse Rise (event)    -   Hemoglobin fall (event)    -   Blood Pressure fall (event)    -   Heparin (channel)    -   PTT (channel)    -   Pulse (channel)    -   Hemoglobin (channel) (as for example real-time derived by pulse        oximetry)    -   Blood Pressure (channel)

Dependency diagrams and dependency lists may be filtered. Samplefiltering include (to name a few):

-   -   Do not display point streams    -   Display only the top-level dependencies (i.e. within FIG. 32        Pulse Response in relation to Heparin 1432, Hemoglobin Response        in relation to Heparin 1434 and Blood Pressure Response in        relation to Hemoglobin 1436)    -   Collapse properties, Rules and Synonyms into occurrence types.        In this case, only the structure is shown, not the specific        sub-elements

In the presence of patient data (e.g. a selected occurrence stream andoccurrence) a dependency tree may be enhanced. For example, for elementsthat may be evaluated down to a single value (e.g. a property or Rule)may display the evaluated value. Elements that represent scope elements(e.g. an occurrence type) may provide a hyperlink into the actualoccurrence that were used to meet that specific scope requirement. Insome cases, such a, a repeating occurrence, the dependency diagram maybe expanded into an instance diagram. In this case single dependenciesare expanded out to a list of all of the occurrences that were found. Inthis way, the instance diagram creates rapid access to the elementswithin the selected occurrence. This may become very useful in verycomplicated images, particularly when the image spans a fairly largeamount of time. The selection of individual nodes within the instancediagram allows the user to navigate to the element within the occurrenceand/or point streams and shifts the parallel time view to be appropriate(e.g. at the right scale and time location p.

In another view, of interest to researchers, the dependency tree may beenhanced to show the statistical significance index (SSI) of each node.In this way, the researcher may recognize the statistical dependencies.If, for example, a root node has an equal or lower SSI than a child node(with reference to a specific condition) then the researcher may want toabandon the more complex image for a simpler version represented by thenode.

FIG. 33 is a user interface model of the occurrence editor specificallyconfigured to define a pattern occurrence, pattern occurrences aredescribed in detail in FIG. 5 and FIG. 7a . The User Interface is verysimilar to the User Interface in FIG. 28 and FIG. 29. (See FIG. 28 andFIG. 29 for all details that are the same as in those figures.) Thepattern occurrence configuration has a unique construction area. Theconstruction area is made up of the occurrence Pattern definition bar1480 and two subsections: the Mnemonic Representation List 1488 and thePattern Sequence Construction Surface 1488.

The occurrence Pattern definition Bar 1480 contains a Phase dropdown1482 and the View Dependencies button 1484 with the same role andfunctionality as in FIG. 28. The Mnemonic Representation List 1488 issimply a list of occurrence types selected to be part of the pattern(for example 1486) aligned with their associated Mnemonic (for example1486). The Pattern Sequence Construction Surface 1488 allows for thearrangements of Mnemonics to represent the pattern occurrencedefinition. If a researcher drags an occurrence type from the occurrenceExplorer onto the Mnemonic List the editor will create an entry in thelist with the next Mnemonic (in, for example, alphabetical order).Mnemonics may then drag mnemonics onto the Pattern Sequence ConstructionSurface to be arranged to describe the pattern. Alternatively, theresearcher may drag an occurrence type from the occurrence Explorerdirectly onto the Pattern Sequence Construction Surface. The editor willfirst search the Mnemonic List to see if a match is found. If a match isfound then the correct Mnemonic is place onto the surface in thelocation indicated by the drop. If the match is not found, then theMnemonic is added to the list and then placed onto the surface in thelocation of the list. One example of the basis for and the process ofincorporation of a new technology into the processor 304 to optimize theefficacy and monitoring of the new technology is discussed. As anexample of incorporation of new technology into the processor 304 asystem and method for detecting enhanced sensitivity to augmentation ofvenous return is described. Although the intravascular volume status ofa critically ill patient is often unknown, assessment of theintravascular volume status using pulse pressure variability, or the useof a pulse variability index has been promoted as effective in thisregard however, as discussed in U.S. patent application Ser. No.11/708,422 (the disclosure of which is incorporated by reference in itsentirety for all purposes as if completely disclosed herein) this canpotentially trend the wrong physiologic action increasing the pulsevariability and thereby providing a false trend resulting in incorrecttherapy. There is a need for continuously monitoring which provides anindication of the intravascular volume relative to the patient's needsand an embodiment of the present invention can provide that function.One embodiment of a system and method for detecting enhanced sensitivityto augmentation of venous return 1500 includes a component of theprocessor 304 1510 which includes a system for detecting intravascularvolume status including a monitor for detecting variations in vascularpressure and/or flow (for example, a pulse oximeter, automatic bloodpressure cuff, or arterial line to name a few), and a device foraugmenting venous return such as a whole leg or calf compression device1512, for compressing at least one extremity to increase venous returnfrom the extremity, and a processor which can be the processor 304 1510or for example a pulse oximeter 1520 for detecting and/or quantifying ameasure indicative of a rise in pulse upstroke 1524 and/or arterialpressure or flow (which can for example include a reduction in pulsevariability 1530 in response to spontaneous or artificial ventilationcycling). In one embodiment a conventional extremity compression device1512 includes a sequential compression devices employed for compressingthe calf s) of a patient to prevent deep venous thrombosis which may bemodified to apply progressive compression which is sustained for a briefperiod. (For example, the extremity compression device may have a DVTprevention mode and a venous return augmentation mode which provides asustained compression applied from a distal to proximal direction.) Forcases with greater acuity compression devices which extend to compressthe thighs may be used. A compression may be timed with anothermechanism for augmentation in venous return such as a fluid bolus toproduce a greater effect with a smaller amount of fluid administered tothe patient. Augmentation may be preceded, followed or combined with amaneuver to reduce venous return (such as a plateau at the end ofinspiration or a brief period of elevated PEEP) to determine therelative sensitivity to venous return in relation to mechanicalventilation. The processor 304 can coordinate the fluid bolus and thecompression to assure proper timing. An indicator 1534 may be providedwhich indicates when the compression device 1512 or another venousreturn augmenter has been activated. The indicator 1534 may for examplebe a pressure transducer, which can generate a time series 1540 of thepressure applied) or may be a step function or time series output fromthe compression device 1512 itself which indicates the occurrence and ortriggering of compression of the extremity. In one embodiment theprocessor 304 1510 is programmed to detect a binary wherein the alphacomponent of the binary includes at least one event inducingaugmentation of venous return (such as the compression of a body part,such as the calf, leg, abdomen, and/or upper extremity, to name a few)and the beta component includes at least one variation of acardiovascular parameter (such as a rise in pulse upstroke 1524 orreduction of pulse variability 1530). Sequential binaries can be derivedalong the time series each time the compression devices acts to compressthe extremity. The occurrence of a new or changing sequential betavalues on the arterial side (indicative for example of increasedsensitivity of cardiac output or blood pressure) to the sequential alphaaugmentation events may provide indication of a decline in intravascularvolume (and therefore an increased sensitivity to venous returnaugmentation). Alternatively the occurrence of a new or changingsequential beta values on the venous side to sequential alphaaugmentation events (which may for example be a decline in the slope ofdecay or an increase in the area under the curve of the central venouspressure in response to leg compression) may provide indication of aexcessive rise in intravascular volume producing increased sensitivityof central venous pressures and pulmonary capillary wedge (or leftatrial pressures) to venous return augmentation. Using this method(which may be combined with the method of monitoring reduction of venousreturn in relation to ventilator changes for example as discussed in theaforementioned patent application) the processor 304 may, for example,compare relational patterns along the time series matrix from presentlydeployed technologies (for example pulse oximeter, central venouspressure monitor, mechanical ventilator, non invasive or invasive bloodpressure monitor, pulmonary artery catheter, continuous cardiac outputmonitor, and the sequential calf compression devices) to provide areal-time indication of intravascular volume and of the cardiovascularsensitivity to variation of venous return reduction and augmentation.The device also provides verification 1560 of actual calf compression inrelation to a time series 1570 of the post surgery timeline.

According to one embodiment for generating a more comprehensive timeseries matrix for analysis by the processor 304, one lumen of the doublelumen cannula can be connected to an oxygen source and the other lumenconnected with a pressure and/or flow sensor so that a time series ofthe nasal pressure can be generated along with a time series of theoxygen thereby and these can be combined with the various time seriesfrom the pulse oximeter. For ease of wearing such a cannula can be ashort connected to a nasal pressure sensor and pulse oximeter worn aboutthe neck (as for example configured like a bolero) where the cannula hasa terminal is connectable to another tube providing connection to asource of oxygen. In one embodiment a single lumen catheter is usedwhich bifurcates at the neck having a one connection for nasal pressureand one for the oxygen source. The presence of pressure induced by thecontinuous oxygen flow and changes in flow is detected as the DCpressure component in the tube upon which is superimposed the ACcomponent of the nasal pressure signal.

According to another embodiment the processor 304 is applied to searchfor evidence of early ventilator associated pneumonia (VAP). The timeseries matrix of the datasets from the ventilator, for example; minuteventilation, 1:E ratio, respiratory rate and effort, FIO2, oxygenconsumption (if available). CO2 production, exhaled CO2, trend oftriggered vs spontaneous breaths, exhaled gas components, to name a few.These may be further combined by the processor with time series ofcardiovascular monitors for example heart rate, pulse pressure (and/orpleth) variability, pulse upstroke, pulse pressure, and maneuverresponsive pressure variability to name a few. These may be furthercombined with time series of laboratory data such as Neutrophilpercentage and/or count, stab (band percentage) and/or count,inflammatory markers, sputum parameters, and time series of otherphysiologic measures or scores such as the SPO2, temperature. These maybe further combined with a time series of other vales such as sputum orBAL score or values, confusion score, and radiographic scores and/orscales. The finding of a cascade of inflammatory—respiratory—and/orcardiovascular augmentation in the absence of other sources of infectionand/or in the presence of a rising sputum volume or purulence score isstrongly suggestive of ventilator associated pneumonia (VAP). Accordingto one aspect of the present invention patients managed with mechanicalventilation are monitored by a patient safety processor 304 whichgenerates a data sets of at least respiratory data and inflammatory dataand intermittently or continuously searches at least both data sets forrelational patterns suggestive of VAP and provides an output upondetection of a relation pattern suggestive of VAP. The relationalpattern can be a cascade comprised of linked or aggregatedperturbations/trends/threshold breaches along a time series matrix.

Objectification is an example of a time series processing method whichmay be employed by the processor 304 to render the time series matrixfreely searchable. As noted, objectification converts the time seriesmatrix into stream s of discrete sequential and/or overlapping objectsof ascending complexity, in 3 or more axes allowing each time series andthe objectified matrix itself to be readily searchable in multipledimensions. An example of the process of time series searching employingobjectification is provided wherein as a first step to objectification;the processor 304 aggregates a set of time series together into a searchvector. The combination of an occurrence type and a search vectordefines the data from which a single occurrence stream is derived. Asearch vector is defined by a time window (whether explicitly or as afunction of specified elements such as “patient stay” or “post-operativeobservation period+1 day”) and a set of time series that apply, bydefinition or by rule, to the tracking of a phenomena within the systemfrom which the time series are derived. For example, a search vector maybe created for respiratory rate and/or amplitude for the hospital stay.The processor 304 can be directed to aggregate all signals from whichrespiratory rate and/or amplitude can be derived. For example, theprocessor 304 can use a signal from a pulse oximeter or may use thechest impedance signal as derived from an EKG signal and/or from adouble lumen pressure sensing nasal cannula, and/or a sound sensor onthe chest or airway, and/or another source of data.

In this way a search vector may include any number of time series. Theprocessor 304 may be directed to “prefer” certain signals such that if acertain signal is found then other signals are ignored. Alternatively,the processor 304 may be directed to aggregate all available signals andallow them to overlap. In this way, a search vector may includeoverlapping time series. For example, if a patient is wearing twodifferent oximeters, the processor 304 may he directed to include bothof these into a single search vector. When there are overlapping timeseries within a search vector, the identification of an occurrence atthe same time (or within a reasonable phase shift specified) is countedas one occurrence for the purpose of search and/or statistical analysis.In this case, both objects are stored and a relational binary is createdbetween them (typed as, for example, “equivalent occurrence”). Theprocessor 304 may identify one of the occurrences as “primary” and allothers as secondary such that statistical analysis and search may notduplicate the multiple recording of a single event within the systembeing monitored. In cases where an equivalent occurrence is expected tobe found, but is not, a divergent binary may be created aggregating thefound occurrence and the search region in which the expected occurrencefailed to be found.

In one embodiment a search sector is meant to accomplish several thingsincluding the following three things. It may allow for the aggregationof time series from the same source where there have been interruptionsof the recording of data. It may allow for the aggregation of timesseries that are from two monitors that are of the same type or whichhave multiple leads. It may allow for the processor 304 to utilizedifferent sources that represent equivalent observation of the samephenomena within the system. On the other hand, in one embodiment of theprocessor 304, the search vector is not used to aggregate two timeseries that represent distinct systemic response to phenomena within themonitored system. The identification of multiple reactions to the samephenomena may be handled by separate but parallel search vectors andallows for the identification of complex occurrence aggregations (suchas images) and their evolution which will generate probabilisticmomentum (explained above).

While the disclosed embodiments may be susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and have been described indetail herein. However, it should be understood that the disclosure isnot intended to be limited to the particular forms disclosed. Indeed,disclosed embodiments may not only be applied to clinical diagnosis ofsystems of physiological failure, but may be applied to any clinicalcondition that may be represented by images as provided herein. Indeed,the disclosed embodiments may be applied to monitor and/or diagnoseconditions in which a patient's condition is generally improving, suchas post-surgical monitoring. Rather, the disclosure is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the disclosed embodiment and as defined by the followingappended claims.

What is claimed is:
 1. A patient monitoring system for monitoring a plurality of patients in a healthcare system and automatically detecting sepsis among the plurality of patients and for displaying sepsis images in the healthcare system, comprising: a plurality of local patient safety monitors, each of the plurality of local patient safety monitors configured to receive physiological measurements from at least a pulse oximeter and a blood pressure monitor for one or more patients, a central patient safety monitor remote from the plurality of local patient safety monitors, the central patient safety monitor having a processor programmed for: receiving data related to a plurality of physiological parameters from a patient over time; generating a respective time series from the data from each of the plurality of physiological parameters; identifying and organizing patterns and threshold violations along the plurality of time series into discrete objects forming object streams; analyzing said discrete objects to identify known relational patterns; converting said relational patterns into instances of relational binaries based on a physiological relationship between the discrete objects; detecting a pattern of sepsis, the pattern being comprised of said relational binaries; organizing and synthesizing said relational binaries into a set of failure images, which as an aggregate whole make up an image representation of the complex and dynamic state of sepsis; and displaying the set of images as a motion image representation of the dynamic evolution of sepsis afflicting said patient on at least one of the central patient safety monitor or one of the plurality of local patient safety monitors upon the detection of said pattern of sepsis.
 2. The system recited in claim 1, wherein both the identified patterns of the data and the relational patterns are converted into objects.
 3. The system recited in claim 2, wherein the sepsis pattern comprises a cascade pattern of said objects spreading within a plurality of physiologic systems.
 4. The system recited in claim 3, wherein the objects are color display objects and wherein the color of the display objects are responsive to severities of the corresponding identified patterns and relational patterns which are converted into the display objects so that the severities of individual perturbations and individual relational physiologic patterns are readily visible along the cascade pattern.
 5. The system recited in claim 3, comprising determining a rate of growth of the cascade pattern responsive to a number and/or severity of new objects being added per unit time to the cascade pattern.
 6. The system recited in claim 3, comprising determining a rate of growth of the cascade pattern responsive to the number of new physiologic systems being affected by the cascade per unit time.
 7. The system recited in claim 1, comprising providing an automatic alarm at one of the plurality of local patient safety monitors upon the detection of said pattern of sepsis for a patient associated with said local patient safety monitor.
 8. The system recited in claim 1, comprising generating a color motion image display of sepsis, the motion image display being dynamically responsive to a severity of the sepsis over time.
 9. The system recited in claim 1, comprising generating a color motion image display of sepsis, the motion image display being dynamically responsive to a severity of the identified patterns over time.
 10. The system recited in claim 1, comprising generating a color motion image display of sepsis, the motion image display being dynamically responsive to a severity of the relational patterns.
 11. The system recited in claim 1, wherein the sepsis pattern comprises a cascade pattern of a plurality of said relational patterns, said relational patterns being derived from physiologic parameters from a progressively increasing number of physiologic systems over time.
 12. The system recited in claim 11, wherein the healthcare system comprises an electronic medical record repository of data which comprises data from a plurality of patients and/or hospitals, and wherein the processor is programmed to identify the patient whose physiologic data is used to generate the cascade pattern and/or the hospital in which the patient whose physiologic data is used to generate a cascade pattern is located.
 13. The system recited in claim 1, wherein the sepsis pattern comprises a cascade pattern of a plurality of said identified patterns of the data said identified patterns being derived from physiologic parameters from a progressively increasing number of physiologic systems over time.
 14. The system recited in claim 13, comprising determining a plurality of states of evolution of the cascade pattern over time.
 15. The system recited in claim 13, comprising determining a medical procedure preceding the cascade pattern wherein the cascade pattern comprises a potential complication of the medical procedure.
 16. The system recited in claim 13, comprising determining a rate of growth of the cascade pattern responsive to a number and/or severity of new identified patterns being added to the cascade pattern over time.
 17. The system recited in claim 13, comprising determining a rate of growth of the cascade pattern responsive to a number of new physiologic systems affected by the cascade pattern over time.
 18. The system recited in claim 13, wherein the healthcare system comprises an electronic medical record repository of data which comprises data from a plurality of patients and/or hospitals, and wherein the processor is programmed to identify the patient whose physiologic data is used to generate the cascade pattern and/or the hospital in which said patient is located.
 19. The system recited in claim 13, comprising, generating a color image display of sepsis, the color image display being responsive to a severity of the identified patterns along the cascade pattern.
 20. The system recited in claim 13, comprising generating a plurality of time segments of the cascade pattern.
 21. The system recited in claim 20, wherein the plurality of time segments of the cascade pattern comprises a set of sequential time segments of the cascade pattern.
 22. The system recited in claim 21, wherein each new time segment of the set of sequential time segments has a longer duration than the time segment which preceded the new time segment.
 23. The system recited in claim 22, wherein each new time segment of the set of sequential time segments contains the time segment which preceded the new time segment so that the cascade pattern grows in duration over time as each additional time segment is added to the set of sequential time segments. 